Condition Based Maintenance and CBM+ Management

Condition Based Maintenance and CBM+ Management

The growth of global competition has led to remarkable changes in the way industrial organizations operate. These changes have affected maintenance and made its role even more crucial for the organizational success. For remaining competitive, industrial organizations are to continuously increase the effectiveness and efficiency of the production processes. Also, the introduction of lean manufacturing has increased concerns regarding equipment availability and, hence, the demand for effective maintenance.

Reliability has been consistently an essential feature in the evaluation of organizational assets (facilities and / or equipments), and as a result, maintenance is a continuous process implemented by the management not only with the core goal of reducing downtime caused by unexpected failures, but also for reducing the associated energy usage while maximizing the performance and the asset life.

In spite of the increasing demand for reliable equipment for production, few industrial organizations pursue the development of strategic maintenance. Moreover, traditional maintenance strategies, such as corrective maintenance, are no longer sufficient to satisfy industrial needs, such as reducing failures and degradations of manufacturing systems to the greatest possible extent. The concept of maintenance has evolved over the last few decades from a corrective approach (maintenance actions after a failure) to a preventive approach (maintenance actions to prevent the failure). Strategies and concepts such as ‘condition-based maintenance’ (CBM) have hence evolved to support this ideal outcome. The CBM techniques are implemented through the identification of unwanted safety and operational failures.

CBM is a set of maintenance actions based on the real-time or near real-time assessment of equipment conditions, which is achieved from embedded sensors and / or external tests and measurements, taken by portable equipment and / or subjective condition monitoring. CBM is increasingly recognized as very efficient strategy for performing maintenance in a wide variety of industries. However, the practical implementation of advanced maintenance technologies, such as CBM, is relatively limited in the industry.

In general, maintenance is defined as all technical and managerial actions taken during usage period to maintain or restore the required functionality of an asset. Maintenance policies are categorized into two main strategic streams namely corrective and preventive. CBM is a sub-division of preventive methodology. Through the utilization of science and technology, CBM exploits the operating condition of equipment for predicting a failure occurrence hence preventing any unexpected downtime and reducing the maintenance cost by avoiding unnecessary preventive actions. The underlining theory of CBM is based on the belief that 99 % of equipment show some sort of indicators prior to the development of a fault. Hence, as per the thorough examination of these signs, an engineer can determine how severe is the problem and how long the machine can perform as normal without any actions being taken to repair the fault.

Maintenance techniques and policies are changing and evolving, arguably more rapidly than any other discipline. In some industries, maintenance and repair procedures and policies are developed post-design or only in response to unpredicted maintenance issues. Now, however, new techniques and changing views on maintenance organization, policies, and responsibilities are emerging. This is in part because of, the management understanding of the impact of equipment failures on safety, availability, facility readiness, and the environment. These changes in attitude have brought about RCM (reliability centered maintenance) policies in an attempt to address a host of reliability issues in order to balance improvement in overall equipment and system effectiveness and control of the programme LCC (life-cycle costs).

The RCM model uses PM (preventative maintenance), RTF (run to failure), and CBM techniques to in an integrated manner to increase the probability that equipment is going to function in the needed manner over its predicted life cycle with a minimum of maintenance. CBM models can be divided into two elements, PdM (predictive maintenance) and RTM (real-time monitoring), which are the pillars of CBM techniques. There are difficulties, however, in implementing CBM, as initial investment costs can be prohibitive. CBM techniques utilized after full reliability and RCM analyses have been completed, provides the programme with the largest ROI (return on investment). Ideally, CBM techniques are implemented during design and development phases and matured throughout the life cycle. However, considerable gains have been realized in implementing CBM in different facilities.

CBM is a maintenance technique which involves monitoring equipment condition and predicting its failure. Several CBM systems are controlled by computers. CBM systems can be used in high-tech industries for corrosion detection. Unlike periodic maintenance where services are based upon scheduled intervals, CBM relies upon actual equipment health to dictate when and what maintenance is needed. In simple words, one can describe CBM as direction of maintenance actions based on indications of asset health as determined from non-invasive measurement of operation and condition indicators. CBM allows preventive and corrective actions to be optimized by avoiding traditional calendar or run based maintenance.

CBM is the maintenance which occurs when the need arises. This maintenance is performed after one or more indicators show that equipment is going to fail or that equipment performance is deteriorating. It is based on using real-time data to prioritize and optimize maintenance resources. Through condition monitoring, a system determines the equipment’s health, and action is taken only when maintenance is actually necessary. Ideally, CBM allows maintenance personnel to (i) perform only currently needed maintenance, (ii) minimize spare part costs, (iii) minimize system downtime, and (iv) reduce time spent on maintenance

Challenges in the use of CBM are (i) initial cost can be high, (ii) invokes a major change in how maintenance is performed leading to organizational changes, and (iii) increased number of parts (the CBM installation itself) which need maintenance and checking. Better operations lead to lower production cost and lower use of resources. As the facilities become more costly, and instrumentation and information systems tend to become cheaper and more reliable, CBM becomes an important tool for running a plant in an optimal manner.

CBM maintenance strategy aims to (i) extend equipment life, (ii) increase productivity, (iii) lower daily operating costs, (iv) improve system reliability, and (v) decrease number of maintenance operations, causing a reduction of human errors.

Hence, as per the CBM theories, it is possible to identify the fault (detection), determine the root cause (diagnosis) and establish the severity and longevity of the equipment’s optimum life (prognosis) through monitoring and evaluating of data collected through different techniques such as vibration, temperature, oil, and acoustic analysis. Moreover, CBM is also able to verify where exactly the fault is, how quickly and to what extent the component is degrading.

Until now, it has been difficult to achieve effectiveness of maintenance operations since there is no information visibility during equipment usage period. However, recently, the emerging technologies such as RFID (radio frequency identification), different types of sensors, MEMS (micro-electro-mechanical system), wire-less tele-communication, SCADA (supervisory control and data acquisition), and PEID (product embedded information devices) are expected to be rapidly used for gathering and monitoring the status data of equipments during their usage period. Advancements in IT (information technology) have added accelerated growth in the CBM technology area by enabling network band-width, data collection and retrieval, data analysis, and decision support capabilities for large data sets of time series data.

Under the new environment, a person can gather the product status and usage data related to distributing route, usage conditions, failure, maintenance or service events, and so on. These data enable people to diagnose the degradation status of the equipment in a more exact way. Hence, using this information gives people new challenging issues for improving the efficiency of the equipment maintenance.

CBM is a maintenance philosophy used by industry to actively manage the health condition of the assets in order to perform maintenance only when it is needed and at the most opportune times. CBM can drastically reduce operating costs and increase the safety of assets needing maintenance. Corrective / reactive maintenance can have severe performance costs, and preventive / scheduled maintenance replaces parts before the end of their useful life. CBM optimizes the trade-off between maintenance costs and performance costs by increasing availability and reliability while eliminating unnecessary maintenance activities.

Some of the important components of CBM are (i) data collection and acquisition, (ii) data transmission and communication, (iii) data storage and warehousing, (iv) data processing and analysis, and (v) maintenance decision support. CBM can be done by (i) gathering equipment status data and monitoring, (ii) making a diagnosis of an equipment status in a real-time way, (iii) estimating the deterioration level of the equipment, its repairing cost which depends on the deterioration level, or its replacement cost, and so on, (iv) predicting the time of equipments abnormality, and (v) executing appropriate actions such as repair, replace, left to use as it is, and disposal.

The components of CBM are an optimized mix of (i) maintenance technologies (diagnostics, and prognostics), (ii) RCM (reliability-centered maintenance)-based processes, and (iii) enablers (total asset visibility). The features of CBM are (i) data acquisition can involve different types of information such as vibration, temperature, pressure, speed, voltage / current, stress / strain / shock, position, and particulate count / composition, (ii) feature extraction calculations can involve FFT (Fast Fourier Transform), data filtering / smoothing, temperature / pressure ratio, efficiency, and mass flow, (iii) detection algorithms alert users to potential problems and otherwise unknown failures, (iv) diagnostic algorithms isolate failures to specific components or sub-systems, (v) prognostic algorithms estimate remaining useful life based on past and future operational profiles and physics of failure models, and (vi) supervisory reasoning algorithms reconcile conflicting information and provide recommendations such as inspections, repairs, parts ordering, and equipment shut-down.

Industrial organizations are beginning to actively pursue the implementation of CBM strategies and technologies to optimize LCC and availability of their systems. PBL (performance based logistics) has also been established by the organizations as the way to support system acquisition and operation. The idea behind these is that organizations meet the performance objectives by using special techniques and models to optimize and improve organizational profit. By utilizing sound CBM techniques, organization can optimize profitability, but only if there is a ROI on the initial capital it takes to implement CBM strategies.

Brief history of RCM and CBM development

Maintenance techniques have evolved from the 1940s to today. CBM is a maintenance technique used to actively manage the health condition of equipment in order to optimize maintenance by performing maintenance only when it is needed and at the most opportune times, hence improving overall system and equipment availability and safety while decreasing operating costs. One cannot talk about CBM without first discussing the concepts and history of maintenance techniques of RCM.

RCM models were developed as a result of the changing world of maintenance, in which managers began to understand and acknowledge the costs of equipment failures on product quality, availability, and ROI. Figure 1 shows the block diagram of the RCM model. The first generation of maintenance techniques were a few, if any, and an RTF or ‘fix it when broke’ theory was the standard. The industry of that time was highly mechanized, and equipment was simple and, in several cases, over designed. This made the systems reliable and easy to repair. There was little need for systematic maintenance beyond simple cleaning, servicing, and lubricating. Fig 1 shows block diagram of RCM.

Fig 1 Block diagram of RCM

The next generation of maintenance techniques evolved from the demand for products, while the availability of industrial manpower sharply declined during World War II. Equipments of all types were more numerous and complex. As industry began to depend on these equipments, down-time became of higher concern, as facilities for all types of products would grind to a halt with a system failure, hence impacting the profitability of the organizations. The cost of maintenance started to sharply increase, with the maintenance costs becoming one of the most significant, if not the most significant, contributor to the overall operating costs. This ushered in the present, and most prevalent, established practice of maintenance techniques, which included overhauls and maintenance planning systems. These maintenance techniques were based on the theory that as equipment ages, it is more likely to fail. The typical failure measure was, and has been (and arguably remains), equipment MTTF (mean time to failure), which provides a probability curve for failures based on operating time. This failure measure supported the view that systems can fail ‘randomly’ at any point in their useful life. This represented the horizontal line in the bath-tub curve. Fig 2 shows a typical bath-tub curve.

Fig 2 Typical bath-tub curve

These types of maintenance techniques, known as PM, typically utilizes MTTF, or the inverse of MTTF, failure rate (F), or Weibull shape (B) and scale (n) parameters, in conjunction with maintenance procedures and the measures of maintainability, including MTTR (mean time to repair), to predict when maintenance activities are required to take place for improving availability, or up-time. In this maintenance technique, over-hauling a system, or replacing parts likely to fail after a certain period of time, is intended to optimize and improve availability at the point where the ‘bath-tub’ curve begins to show an increase in failures, or end of life. Performing maintenance or over-hauls prior to this point, or during a system’s useful life, most likely do not optimize the availability and can tend to increase the maintenance costs. Fig 3 gives is an industry accepted graph showing the qualitative relationship between the frequency of PM and the total costs. Exceptions to these types of PM techniques are when the equipment failures have extreme safety consequences, like in the nuclear power industry. In these cases, the cost of PM is normally a secondary factor.

Fig 3 Time between maintenance intervals versus cost

There can be difficulty in determining the cost / benefit ratio of PM. Although increasing the maintenance intervals almost immediately decreases the overall cost, the negative results of increasing that interval do not be seen until equipment replacement and repair costs and down-time increases, at a much later time, and erases the short-term savings. However, PM techniques alone do contribute to the improved system availability and tend to minimize maintenance costs. New studies have changed people’s beliefs and understanding of age and failure of equipment. It is becoming apparent that the connection between the operating age of equipment and how likely they are to fail is diminishing. As the decision support tools and data analysis tools become increasingly more sophisticated, and the systems are being designed with higher emphasis on high reliability and maintainability, the equipment reliability and maintainability modelling also evolve. Utilizing CBM techniques can assist not only in the reliability and maintainability modelling, but in decreasing overall system down-time.

CBM is not a new maintenance technique. The first generation appeared in the late 1940s, as implemented by the Rio Grande Railway Company. The company achieved economic success and reduced engine failure by performing maintenance whenever glycol or fuel was detected in the engine oil. The US army was impressed by the relative ease with which physical asset availability could be improved and adopted those techniques and developed others. During the 1950s, 1960s, and early 1970s, the emerging technology industry began providing training, products, and services which came to be known as ‘predictive maintenance’.

The next generation of CBM coincided with the dawn of the ‘information age’. ‘Technology people estimated that, if simple physical measurements, such as vibration amplitude or oil viscosity, can provide such useful benefits, then collecting the data in computers and trending it over time are likely to provide a far deeper insight into the state of the health an equipment. Hence, the 1980s and 1990s witnessed a soaring rise in the use of computers, software, and data collections in the maintenance departments throughout the industrial world. This is the present understanding and application of CBM.

CBM elements and techniques – CBM is a failure management strategy for a particular failure mode which meets criteria such as (i) the potential failure is clearly defined, (ii) failure interval is identifiable, (iii) the maintenance task interval is less than the failure interval and physically possible, and (iv) the time between the discovery of the potential failure and the occurrence of the function failure is long enough for the maintenance action to be taken to avoid, eliminate, or minimize the consequences of the failure mode.

Whereas PM addresses age-dependent failure probabilities, CBM addresses failures which can be measured by one or several indicators. When applying maintenance efforts (people, processes, and tools) in a CBM environment, maintenance is based on the actual condition of the equipment against the age of the equipment, so that equipment in good condition does not need to be maintained as frequently as equipment which has reached the predicted age of deterioration. The core of CBM is using test equipment or statistically modelling data to predict the condition of equipment. The vision of the CBM application is to enable equipment to achieve nearly zero break-downs. This transforms traditional maintenance practices from RTF to PdM and prevention of failures. CBM utilizes failure history to predict break-downs before they happen to prevent future failures from occurring in the field where repairs are costly and operations are impacted.

Real time monitoring (RTM) which is an element of CBM is based on the idea that equipment can be evaluated while remaining in service, which drives down the overall cost of maintenance. As an example, a generator can be monitored based on a number of parameters in real time, such as temperature, cooling gas density, bearing vibration, lubricating oil condition, and others. Statistical analysis of the collected data allows diagnosis of impending failures. These failures, also known as incipient faults, cannot be predicted by human senses. Other equipment such as circuit breakers, relays, and switches are not readily assessable utilizing RTM techniques.  The valid contender for RTM is to satisfy both these criteria namely (i) equipment is to be critical or expensive enough to warrant the cost, purchase, and installation, of monitoring hardware and software, and (ii) analysis of the parameters monitored is to provide meaningful diagnostics and prognostics.

PdM is another component of CBM. PdM trending techniques have been used to confirm maintenance decisions which have previously been based on expert opinions. PdM uses test results taken from PM techniques. Statistical analysis results are evaluated, and a prognosis is developed indicating the need for increased, decreased, or even elimination of maintenance intervals. While these techniques have frequently found problems which have not otherwise been identified, PdM can actually slightly increase daily maintenance costs for some equipment because of the additional analysis needed.

There are different kinds of techniques to be applied in data processing, diagnostics, and prognostics for implementing CBM. In CBM, there are three kinds of approach namely (i) data-driven approach, (ii) model-based approach, and (iii) hybrid approach. Data-driven approach has the ability to transform high-dimensional data into lower dimensional information. It is also known as the data mining approach or the machine learning approach, which uses historical data to automatically learn a model of system behaviour. However, this approach has the dependency on the quality of the operational data and there is on physical understanding of target equipment. On the contrary, model-based approach has the ability to incorporate physical understanding of the target equipment. It relies on the use of an analytical model (set of algebraic or differential equations) to represent the behaviour of the system, including degradation phenomenon. But it has the limitation in the point that it can only be applied to specific types of equipments. The condition monitoring and analysis part of CBM can be broken down into two types namely (i) diagnostics, and (ii) prognostics.

Diagnostics deals with post event analysis and includes fault detection, isolation and identification when it occurs. Fault detection is a task to indicate whether something is going wrong in the monitored system, fault isolation is a task to locate the component which is faulty, and fault identification is a task to determine the nature of the fault when it is detected.

For better ensuring readiness and decrease down-time costs, health monitoring technologies are developed for CBM of individual equipment items within a facility. A common way of detecting faults in mechanical equipment is to compare measured operational vibrations to a healthy reference signature in order to detect anomalies. The main challenge to this approach is that several equipments are not equipped with the sensors or acquisition systems to acquire, process and store mechanical data. Hence, for the implementation of health monitoring, one is to overcome the economic and technical barriers associated with equipping facilities to continuously monitor responses or to poll responses from the sensors.

There are different equipment fault diagnostic approaches which include statistical approaches, artificial intelligence approaches, model-based approaches, and SPC (statistical process control) techniques etc. Since there are several diagnostic techniques, one of the difficulties is to understand and choose the right technique. Another issue is to assess the success and cost of these techniques.

A major challenge of diagnostics in CBM is to identify correctly which conditions of the equipment are to be monitored. Once identified, appropriate sensors combined with equipment-level automated computational capability are needed to trigger warnings accurately. Sensor integration, component traceability regardless of facility and configuration management are also issues which are to be addressed to have an effective CBM capability.

Prognostics deals with fault prediction such as identifying whether a fault is impending and estimating how soon and how likely a fault is to occur. Prognostics achieves zero down-time performance more efficiently than diagnostics. Diagnostics, however, is needed when fault prediction fails and a fault occurs.

There are two main prediction types in equipment prognostics. The most obvious and widely used prognostics is to predict how much time is left before a failure occurs given the present equipment condition and past operation profile. This is called RUL (remaining useful life) of the system or sub-system. Similar to diagnosis, prognosis also falls into three categories namely statistical approach, artificial intelligence approach, and model-based approach. Some of the statistical techniques used are PHM (proportional hazard model), PIM (proportional intensity model), HMM (hidden Markov model), continuous-discrete stochastic process, and gamma process etc. Some of the AI (artificial intelligence) techniques applied to RUL estimation are neural networks and neural fuzzy approaches. Another class of equipment fault diagnostic is the model-based approach, including the hierarchical modelling approach and non-stochastic model.

Accurate identification of RUL is a challenge which needs to be addressed in the prognostic area. Another difficulty is the prognostic algorithm development and sensor integration and overall system integration. Also, another challenge is to evaluate, understand and test the level of maturity of the diagnostics and prognostics techniques. Expansion of CBM models from the consideration of single components to complete systems can be another potential challenge.

Recently, in a study, CBM techniques have been classified into three categories based on their data source. These are (i) the existing sensor-based maintenance technique, (ii) the test-sensor-based maintenance technique, and (iii) the test signal-based maintenance technique.

One of the studies has proposed the frame-work of watch-dog agent for predictive condition-based maintenance by realizing multi-sensor assessment and prediction of equipment or process performance. The concept of watch-dog agent is based on its degradation assessment on the readings from multiple sensors which measure critical properties of the process or equipments under a networked and tether-free environment. The watch-dog agent is an embedded system which has algorithms to autonomously assess and predict the performance degradation and remaining life of the equipments and components.

CBM evolution to CBM+ – CBM+ is the initiatives which strive to push inspection and scheduled replacement failure strategies toward CBM (to improve safety readiness and cost). CBM+ has broadened this definition by including technologies such as ‘interactive training’, IUID (item unique identification), and ‘integrated information systems’. All activities within CBM+ can be broken into three categories, namely (i) CBM analysis tools, such as RCM, and FMECA(Failure Modes, Effects, and Criticality Analysis), (ii) CBM enablers, such as active or passive sensors, and (iii) CBM ancillary enablers, such as re-designs of systems which remove failure modes that cannot be monitored.  While RCM uses CBM as a failure management strategy, the CBM+ focuses on providing the support needed to permit CBM. These RCM, CBM, and CBM+ strategies are shown in Fig 4.

Fig 4 RCM, CBM, and CBM+ strategies

CBM+ strategies applied allow for maintenance performed based on evidence of need provided by RCM analysis and techniques and other enabling processes and technologies. These strategies use the systems engineering approach to collect data, enable analysis, and support the decision-making process for system acquisition, sustainment, and operations. They range from addressing challenges, by improving diagnostics and prognostics or similar, to fostering and developing new support concepts, such as anticipatory maintenance.

The range of technologies and strategies included in CBM+ is broad, which can make it difficult to focus on a core strategy or goal. The CBM concept encourages the need-driven approach to system support and forms the foundation of CBM+. The ‘plus’ is intended to encompass a variety of equipment and maintenance process improvements which are possible through (i) more disciplined approach to maintenance planning, (ii) development of sensor technologies, (iii) better maintenance collection and analysis techniques, (iv) more capable and user-friendly maintenance aids, and (v) integration of maintenance and other operational facilities into the programme structure.

Essential elements of CBM+ – The elements of CBM+ can be placed in two primary categories namely (i) operation / management, and (ii) technical. There are six main sub-groups within these categories. The first three are in the operation / management category and the last three listed fall within the technical category.

The first is policy and doctrine.  CBM+ is to be implemented for improving maintenance agility and responsiveness, increasing operational availability, and reducing life-cycle total ownership costs. The organization is required to establish a CBM+ environment for the maintenance and support of facilitiues by establishing appropriate processes, procedures, technological capabilities, information systems, and management concepts.

The second is organizational strategy. The basis for CBM+ is a focus on improving the organizational process of maintenance with the objective being improved operational performance as a result of increased maintenance effectiveness. Identifying organizational needs and strategies is the key to this element of CBM+. Processes are to add value, prevent unnecessary maintenance activities, and increase effectiveness and efficiency. Once the CBM+ in the organization is developed, it becomes the necessary tool for validating and supporting the CBM+ need.

The third is RCM relationship. There is a close relationship between RCM and CBM+. The RCM analytical approach helps the maintenance manager identify potential failures and support the selection of viable courses-of-action. RCM tools help define the optimal failure management strategies and provide input to the organization for implementation of CBM+ strategies.

The fourth is hardware and software infrastructure tools. When measuring equipment condition, the ideal operation health of specific equipments or components is determined based on inputs from sensors or sensing systems. This information is used within an infrastructure of hardware, software, and related tools to make maintenance or operational usage decisions. Achieving the full benefit of CBM+ needs that integrated infrastructure be in place to support.

The fifth is the architecture for CBM+. The concepts, policies, procedures, practices, systems, and technologies of CBM+ need integration, connectivity, and a common purpose across functional, organizational, and physical boundaries. The complexity and diversity of the components of CBM+ mandate a structured plan for ensuring complete and effective implementation of all needed elements in a reasonable time-frame. An architectural representation can provide a holistic view and a mechanism for enabling the execution of the design and development as well as for communicating the initiative goals to managers, operators, and stake-holders.

The sixth is the open systems and data strategy. The term ‘open systems’ refers to the design of hardware, software, and organizational processes based on industry and statutory standards which are supplier and equipment inter-dependent. The open systems concept is necessary for CBM+ since a comprehensive implementation is frequently executed in an environment which includes different sensor technologies, multiple information systems, different data models, collection mechanisms across organizational boundaries, and different organizational system environments.

For helping integrate this set of components, a number of standards relating to CBM have been established by the ISO (International Organization for Standardization), IEEE (Institute of Electronics and Electrical Engineer), and SAE (Society of Automotive Engineers). The MIMOSA (Machinery Information Management Open Systems alliance) has also established specifications and data models in support of condition monitoring. Data strategy is also necessary for CBM+. The degree of data management depends on the quantity of health assessment and predictive activity needed. Several possible approaches exist for data storage and interchange, and aggregation can occur across the system. Data collection strategies include trend analysis, pattern recognition, tests against limits and ranges, and statistical analysis.

CBM+ enablers – The enablers of CBM+ are those tools, equipment, and techniques which allow for successful results from CBM+ strategies. There are several examples of these enablers, including sensors and algorithms, data collections, maintenance information systems, information tools, engineering analysis, and system integration. Fig 5 shows examples of CBM+ enablers.

Fig 5 shows examples of CBM+ enablers

Sensors include the on-line, embedded, or off-line, portable equipment interfaced to the facility, plus the software programmes to facilitate the analysis. Ideally sensors have a low failure rate and high confidence in the data measurements. Data collection enables statistical data analysis to determine failure indication and abnormal performance. Data collection can be within the system, as in a data bus or a PLC (programmable logic controller). Data collection at a facility-level or organizational level includes collection of data such as frequency of spare parts ordering (and cost) and the time and place of the destination of these orders. As an off-line system, or in intermediate or department level, data collection can include quality control issues, support equipment requirements, and storage support availability.

Maintenance information systems network tools identify both up-line reporting / recording and down-line support. In other words, implementing tools such as CMMS (computerized maintenance management systems) to track maintenance and calibration activities enables CBM+ strategies by tracking PM, unscheduled maintenance, and calibrations. Support is also to include training for CMMS use to the maintenance personnel. Information tools can enable CBM+ strategies by providing IETMs (interactive electronic technical manuals), PMAs (portable maintenance aids) and computers, AIT (automated identification technology), and (SIM (serialized item management) tolls for the maintenance personnel to allow for efficient and effective identification of impending failures or maintenance needs.

RCM analysis results collected from actual and like systems identify trends to provide maintenance plans. These analyses fall under the engineering analysis umbrella of CBM+ enablers. Maintenance plans are to be iterative in response to engineering analyses. Finally, system integration links facilities with maintenance through the increased capabilities of computer systems and connectivity tools for providing faster response and better material support. An example of this enabler is the linkage of equipment orders for spares and support equipment with predicted maintenance or operations activities. These CBM+ enablers allow for CBM+ to be implemented appropriately by providing the optimal results.

Objectives and metrics of CBM+ – Strategies of CBM+ provide the opportunity for improving the organizational processes, specifically improved maintenance performance to include benefits such as increased productivity, shorter maintenance cycles, lower costs, increased process quality, improved system availability, and improved reliability of equipment. With this in mind, four metrics have been developed for determining the overall life-cycle sustainment impact and outcome of CBM+. These metrics are given below.

The first is the MA (material availability). MA is a measure of the percentage of the total inventory of a facility which is operationally capable (ready for tasking) of performing an assigned activity at a given time, based on material condition. Mathematically, this is expressed as the number of operational end items divided by the total population. MA also indicates the percentage of time a facility is operationally capable of performing an assigned activity. This is expressed as up-time divided by the sum of up-time and down-time. Material, or equipment, availability is a KPP (key performance parameter) associated with KSAs (key system attributes) of material reliability and ownership costs. This is part of an IPSP (integrated product support planning) strategy, or IFS (integrated facility support) strategy.

The second is the MR (material reliability). MR is a measure of the probability which the facility performs its intended function without failure over a specific interval. MR is normally expressed in terms of MTBF when the facility is in design or non-operational. Once operational, MR can be measured by dividing actual operating hours by the number of failures observed during a specific interval.

The third is OC (ownership cost). OC balances the sustainment solution by ensuring that O&S (operations and sustainment) costs associated with MR are considered when making design and operational decisions.

The fourth is MDT (mean down time). MDT is the average total time needed to restore a component to its full operational capabilities. MDT includes the time from reporting of a component failure or NOC (non-operation capable) status to the component being given back to operations for production to operate in an FOC (fully operation capable) status. MDT is an important measure in OA (operation availability), and, hence, MDT can be extended to equipments operating in POC (partially operation capable) status while achieving all operational needs. For example, a facility with four pumps can be able to meet operation needs with only two pumps operating, with a third in back-up. A failure of the fourth pump gives the facility POC status, but not impact the operational availability, hence allowing the operation to continue. Fig 6 shows the relationship between CBM+ objectives and the life-cycle sustainment metrics.

Fig 6 CBM+ objective against life-cycle sustainment metrics

CBM / CBM+ strategies, activities, and timeline – It has been proven repeatedly that the most effective and efficient maintenance plans are developed during the design and acquisition of a facility or an equipment, as the correct processes and technologies are incorporated up front. Redesign efforts are expensive, as are implementation of new maintenance plans when a facility is operational. However, as discussed previously, initial cost impacts for implementing new maintenance policies as a result of CBM are not to deter any maintenance or maintenance practitioner, or manager, from taking advantage of CBM strategies and opportunities when possible and when the long-term gains exceed the short-term expenditures.

CBM implementation is done in three phases which complement the overall total system life-cycle design and acquisition strategy. These phases are (i) planning and technology selection, (ii) implementation, and (iii) operations. The initial efforts with CBM planning and technology selection phase focus on CBM concept familiarization, ensuring that management and employees at all levels specifically the maintenance personnel understand CBM, are supportive of CBM objectives, and are planning and developing the basic steps needed to initiate the effort. As with all initial efforts, change management is key, and ‘buy in’ from the bottom-up can be a more effective approach than forcing change from the top-down.

Getting management support is necessary. Since the CBM strategies and technologies are gaining popularity and proving their long-term impact on operations and sustainment costs for several programmes, facilities, and equipment, it is easier to get management support. Several managers accept CBM strategies, to varying degrees, but can be unfamiliar with the specific changes needed to implement these strategies to achieve results. This puts CBM supporters in the role of a CBM promoters.

Having high quality communication skills, energy, and knowledge in CBM strategies and its application to the programme, are all which the CBM promoters are required to have. Ensuring that management receives the proper quantity of briefings with the right reasoning is necessary in getting through this hurdle. Getting first the ‘buy-in’ of maintenance technician can help with this step. A maintenance technician can better quantify the impact of system down-time, supply chain issues, support equipment issues, and maintenance issues than is done by a maintenance manager. This is also particularly important to the manager’s understanding of the technicians and funding impacts as the CBM initiative progresses.

Next is the performing of RCM and reliability analyses. Reliability analysis is to consider trade-offs amongst time to failure, facility performance, and facility life-cycle costs. The primary types of analyses needed for CBM include RCM, FMECA, and FTA (fault tree analysis). One is also to consider an approach with RBDs (reliability block diagrams) and IMA (importance measure analysis) in order to determine the components which are contributing the highest to facility non-availability. This methodology can help quantify component failure contributions and the impact on the ROI. Although not explicitly called out, a LORA (level of repair analysis) can also identify the overall maintenance structure and determine where an item is going to be replaced, repaired, or discarded based on cost considerations and operational readiness.

Forming a CBM team is the next step. Throughout the organization and industry, the IPT (integrated product or process team) is an effective way to gather the personnel and skills needed for achieving the CBM initiatives. CBM is not a one-dimensional discipline, such that brining in only persons focused on one aspect of CBM, such as sensor technology, does not provide the range of expertize needed for effective implementation. An IPT of the CBM includes, at a minimum, systems engineering, reliability engineering, safety, data management, supply chain management, maintenance and operational engineering, and training and certification. Persons with clear understanding of the costs associated with each and budgeting and funding are to also be included.

Next activity is the identifying of the target application of CBM. In several cases, CBM can need considerable up-front funding and resources from the organization under maintenance head. Hence, CBM promoters selectively focus on equipment with an anticipated high ROI in improved performance, improved facility life, and more efficient maintenance capability, and overall reduction of life-cycle resource costs. This is where the reliability analysis, specifically the RBD and IMA provide important input, regardless of whether the implementation is for new acquisition or operation programme. Other targeted areas can be in those facilities with the highest maintenance work-load or support equipment needs.

Accomplishing the proof-of-concept is the next step. Small scale demonstrations of primary CBM methods and technologies are advisable for justifying time and funding resources for primary CBM implementation. Demonstration of CBM planned methods and technologies give managers a higher degree of confidence in the likelihood of future success. Documentation of test results provides support for future implementation and funding.

Next is the preparation of the CBM implementation plan. The format for a ‘CBM plan’ is to include a comprehensive statement for the planned scope of the CBM application, general supportability objectives for major maintenance activities, a description of how initiative goals and objectives are to be achieved, requirement statement and planned design approaches, identification of key factors external to the organization and beyond their control which can considerably affect goal and objective achievement, a description of the programme evaluation process (including metrics) to be utilized in evaluating progress, and a plan of action and milestones.

Examination of new technologies is the next step. The most difficult task for CBM implementation can be to correctly match available hardware, software, and supporting technology solutions to the needs of anticipated and future maintenance processes. A decision analysis, or trade-off analysis, is needed to be performed for determining the optimal solution for the required capabilities against cost, time, and implementation difficulty. Decisions on technology selection is always to be made considering the need to meet functional needs.

Next is the development of the data strategy. This defines the approach and mechanism for managing the condition and related data needed for accomplishing condition-based analysis. The architectural interface views for data management, storage, and exchange are to be completed.

Development of the architecture is to be done next. Once the implementation plan for CBM has been approved, the architectural views, descriptions and profiles are to be constructed. Managers rely on the architectural representations to identify personnel training topics, assess progress for each process component, reallocate developmental resources, integrate different process components, and explain details of the initiative to outside reviewers.

Setting of the life-cycle metrics is the next step. Life-cycle sustainment metrics provide the quantitative tools for tracking of the CBM implementation and operation. High-level diagnostic metrics are to be developed for ensuring the achievement of overall performance and cost goals. Quantitative measures are to be in the areas of meeting the strategic needs of the organization, needs of individual facilities, addressing internal organizational performance, and addressing process improvement initiative results.

Next step is ‘develop deployment and support’ strategy. CBM implementing personnel are to define and develop the projected deployment schedule, including the expected training and installation dates. A well-documented, yet flexible deployment plan is critical to success. The deployment plan is to be iterative. If installation is not working, roll-back efforts can be made to try to install again at a future date. Data conversion is a critical task for the deployment of new capabilities.

Completion of the operation case is the next step. An operation case analysis (OCA) identifies functional and supporting technical alternatives and presents economical and technical arguments for selecting alternatives over the life-cycle to achieve the organizational objectives or management direction. This is a necessary tool for supporting management decisions and helping justify the programme and budget inputs.

Next step is the development of the resources’ strategy and an integrated budget. It is critical that from the beginning CBM is to be promoted with stake-holders and facilities in mind. This needs the identification of resources and budget expectations. Depending upon where the CBM initiative is applied, applicable funding is to be from research and development, procurement, or operations and maintenance (O&M) appropriations. CBM managers are to ensure that the validated resources are included in acquisition need documents as early as possible.

Planning and technology selection phase timeline is the next step. Several of the process steps can be performed concurrently and others are to be completed prior to certain steps, e.g., identification of the target application is to be only accomplished during the latter part of RCM and reliability analyses, so that the optimal application can be identified based on its most significant contribution to the high maintenance costs or other important factors. Likewise, management support, along with resource strategy and integrated budget development, is to be a continuous and iterative effort. As more details are developed through data strategy development and examination of new technologies, management is to be updated and approval sought for the recommendations from the IPT of the CBM.

Fig 7 shows this timeline and the different overlapping process steps which can and are to be performed. The overall timeline in terms of months or years is dependent upon the complexity of the facility or equipment examined and the resources available to initiate the planning. This can range from six months to nine months for a less complex facility during acquisition phase to one and a half year to two years for more complex facilities with significant management education for buy-in of CBM strategies. Fig 7 shows planning and technology selection phase timeline.

Fig 7 Planning and technology selection phase timeline

CBM implementation phase starts after planning and technology selection. Building on the achievements from the planning phase, the CBM implementation team is required to manage a time-phased implementation of process changes, technology insertion, organizational realignments, and equipment changes. Implementation is to proceed as per the planned milestones from the planning and technology selection phase. The principal activities to be executed during the implementation phase are described below.

The first activity is the acquisition of CBM technical capabilities. This activity is to include acquisition of sensors, communications, and data repositories. This is one of the most visible and expensive elements of the implementation effort. Operational functional managers are to participate in this activity, along with technical and maintenance managers, for ensuring that the selected technologies meet the operational needs and that hardware components can be integrated into the overall architecture of the maintenance and other supporting processes. Cost considerations, ROI, availability of resources, and delivery lead-times are also to be considered.

The second activity is to acquire health management software. The same basic guidelines applied to hardware, such as inter-operability, cost, and satisfying function needs, are also to be applied to software acquisitions. Software and hardware packages are to be compatible, with software functionality validated first and supporting hardware integrated to complete the total package.

The third activity is the demonstration of data management approach. Majority of the CBM elements are focused on improving data production, communication, storage, access, and use. A functional demonstration of the data management process to the users is to occur as early as possible in the implementation phase. This demonstration is to include a review of a statistically significant range of data in a data-base and health management software test runs against the data-base. This is a building block of the foundation for user confidence in the ROI for CBM.

The fourth activity is the revalidation of the RCM and the reliability analysis. A continuous review process ensures periodic revalidation of initial reliability assessments. During the design or acquisition process, this continuous review is necessary to determine appropriate changes to maintenance procedures and approaches based on facility re-design, equipment and component modifications, operational and organizational changes, technological advances, improved failure data, and an improved operational capability.

The fifth activity is the demonstration of element inter-operability of CBM. This is the demonstration of the effective inter-operations of such things as hardware, software, data analysis, and failure trends. This is an iterative process, as hardware and software elements of CBM are acquired and the data management mechanism is put in place, CBM implementation personnel are to test the information exchange capabilities using as much of the condition data and analytical information derived from sensor sources.

The sixth activity is the demonstration of CBM functionality. Once the component elements of CBM initiative are acquired and tested, the next step is to test and validate system inputs, outputs, and analytical products against approved metrics. Demonstrations are to assure the implementation personnel and users that CBM elements produce results which are accurate, timely, and meet the expectations of the user.

The seventh activity is the completion of the pilot programme field test. There is no substitute for process testing in an operational environment. Pilot testing is to be done at an operational location which permits the users to participate in the new process in a familiar setting. The pilot test environment is still io be more controlled than actual operations, such that a comprehensive test plan structure is followed, test activity and results are tracked and fully documented, including operational user commands, input and output test data are to be screened, with out-of-tolerance data clearly identified, and specific testing time-frame is to be established. Documentation of these test results help assess whether the results of the reliability analysis which determines the maintenance actions are appropriate for the tested equipment.

The eight activity is to resolve performance and cost issues. The demonstration and pilot test efforts provide the input for modification of performance objective and identify areas where additional costs or reallocation of resources are necessary. If resource changes cannot be made, then the impact on the CBM Implementation plan is to be determined and communicated with management.

The ninth activity is the training of the stake-holders and users. Training is a very important part of deployment. Stake-holders and users need training in both learning how to work with the condition-based applications and on how to work within the condition-based process. Training plans and schedules are to follow the implementation milestones. Training plans are to maximize the use of new learning techniques, simulation technologies, embedded training, and distance learning systems.

The tenth activity is the revision of the implementation plan. The CBM implementation plan is to be responsive and kept current and aligned with management decisions, resource availability, acquisition of essential CBM elements, and the achievement of the milestones.

The eleventh activity is the update supportability strategy. Facility supportability is highly dependent on the maintenance plan. Continuous analysis and revisions to the maintenance plan can balance operational support resources. Reviews of readiness degraders, equipment maintenance data, maintenance programme schedules and execution, and industrial coordination to identify and assess new maintenance methods and technologies are necessary to update supportability strategies.

The twelfth activity is to acquire full production capability. The full range of planning, acquisition, testing, and demonstration actions are to be successfully accomplished prior to approval of acquiring the full scope of CBM capabilities.

The thirteenth activity is to accomplish CBM deployment. Deployment is to be executed as per the deployment and supportability strategy plan. CBM implementation personnel are to ensure that the user departments are fully prepared to receive and operate the planned CBM capabilities.

The fourteenth activity is the implementation phase timeline. Majority of the implementation activities are to be performed consecutively, with testing, inter-operability demonstrations, pilot field testing, acquisition of technologies and deployment needing to take place at specific times with specific pre-requisites. However, some activities like iterating CBM Implementation plans and supportability strategies are to be performed constantly through the implementation phase in response to equipment and support availability and management decisions.

Fig 8 shows the timeline for the CBM implementation phase. As with the planning phase, a time-frame for this is largely dependent upon the complexity of the facilities and equipment. In addition, the implementation phase includes activities which are somewhat outside of the control of the programme and management, like acquisition of hardware and software. This impacts the overall timeframe. For less complex facilities, the implementation phase can run from one-and-a-half year to two years, including training and testing. A more complex solution, or internally developed solutions, and complex equipment can take up to two-and-a-half years. This can take longer because of the acquisition and training needs.

Fig 8 CBM implementation phase timeline

The CBM operations phase is the third phase. Once deployment of the first significant increment at an operational user location is completed, the operations phase begins and ends with the cessation of use or replacement of the CBM capability. The CBM operations phase has the following activities.

The first activity is to continuously analyze condition-related data at equipment, facility, and organizational levels. The CBM strategy becomes institutionalized once sensor-based technologies and data management strategies are acquired and installed. This provides the ability to analyze collected data and produce effective decision support information. CBM implementation personnel are continue to pursue the development and installation of all the necessary CBM elements.

The second activity is to revalidate RCM and reliability approaches. As with the analysis of condition-based data, continuous monitoring of performance and cost metrics is necessary for ensuring a positive ROI and the most effective approach to satisfying organizational maintenance needs.

The third activity is to develop performance base-lines. The capability to collect, track, and assess a base-line of equipment maintenance information sufficient to populate and continuously update performance and cost metrics databases is key to providing ROI for CBM. CBM works best when failures, failure modes, and failure indicators are well understood. As with reliability testing, one tends to learn more with failures, than without. Developing performance base-lines establishes a historical data repository of key performance and cost information to support maintenance programming and budgeting submissions, OCAs (operation case analysis), and validation of maintenance strategies.

The fourth activity is to continuously review the CBM metrics. As CBM initiatives are implemented, it is important to track progress against organizational objectives for ensuring that the effort is meeting management expectations. CBM metrics are to be consistent with and supportive of operational objectives, specifically, maximizing facility and equipment readiness and availability, improve facility, equipment, and component reliability, reduce life-cycle ownership costs, and reduce mean down time. Metrics can be used to direct or reassess CBM management efforts for evaluating the effectiveness and efficiency of the CBM initiative.

The fifth activity is to refresh enabling technologies. Technology advancement occurs rapidly and needs CBM implementation personnel to develop a technology refreshment strategy for providing long-term, cost-effective support and operations ahead of the obsolescence curve. A proactive approach to managing technology updates include improving technology surveillance, leveraging industry technology advancements, minimizing product obsolescence impacts, developing realistic and achievable technology refreshment planning schedules for the selected facility and safety critical products, and building and maintaining a knowledge base which contains lessons learned that can easily be accessed. There are down-sides to technology refreshment, such as expensive modifications and increased configuration management (CM) for multiple versions of hardware and software.

The sixth is to revalidate human interfaces. There can be in the organization mismatches between technology capabilities and operator abilities to properly understand and make the best use of the technologies. Adequate training can help lessen this risk, but periodic reviews of manual input and output procedures sometimes reveal human interface deficiencies.

The seventh is to periodically update CBM operation case. As the life-cycle of the system or equipment progresses, maintenance personnel are to revisit the operation case to see if the factors validating a particular level of CBM implementation are still applicable and provide quantified justification for revised inputs to budget updates.

The eighth is to continuously update resources strategy and integrated budget. Resource needs for maintaining and updating CBM capabilities change and new systems and equipment are needed, maintenance plans are revised, new technologies are developed, and reliability assessments are modified. It is still necessary to have the CBM strategy since the stake-holders and personnel change to ensure management support. As facilities and equipment reaches the end of their operational life cycle, phasing out investment (financial and resource levels) is sensible.

The ninth is to optimize maintenance strategies. New policies and procedures, operational experience, technology updates, organizational changes, funding availability, and other factors necessitate reassessment of a number of the initial approaches. From initial deployment to cessation of CBM strategies, documenting lessons learned and looking for new ways to improve CBM techniques and adopt updated enabling technologies only improve the CBM process.

The tenth is operations phase timeline. Unlike the planning and implementation phases of CBM, the activities in the operations phase are to be continuous. However, the activities start and stop depending upon the resource availability.

Benefits, drawbacks, and challenges of CBM / CBM+ – The potential benefits of a CBM system are described below.

The first is the real-time assessment of equipment health. With the capability of providing useful reliability information, to the operator, CBM can be readily integrated into the control systems of the facility and equipments. The value of CBM to the operational manager lies primarily in decision-making in order to increase the effective-ness through reducing equipment down-time.

The second is the improved operational availability. CBM methodology can increase the operational availability of the facilities by eliminating unnecessary inspection or maintenance as well as improving the facility performance. Diagnostic and prognostic technology can reduce the trouble-shooting time and prognostics can reduce the time to acquire a replacement part based on the ability to order spares, while the facility is still functional.

The third is more predictive / decision making. In a dynamically changing training or contingency scenario, time and information are crucial elements needed by the managers to make key decisions. Since CBM can provide useful predictions on the health of equipment, the manager has the potential to make better decisions on operational employment of equipment in support of the facilities. More importantly, a prognostic capability can provide an indication of the existence of a fault during the operation of a facility. This facilitates better planning, as it can allow the maintenance personnel to replace the potentially (or anticipated) defective equipment in a planned manner. This contributes to maintaining the operational availability of the facility as well as maintaining the reliability of the equipment because of avoiding an operational failure.

The fourth is reduction in maintenance induced errors. A portion of normal trouble-shooting can be automatically performed by the CBM system, which saves man-power and helps reduce mis-diagnosis. Repair personnel perform fewer preventive maintenance actions such as inspections, adjustments and part replacements since a CBM system localizes faults. There is also a reduced need for data entry by both the maintenance personnel and operators.

The fifth is the anticipatory supply chain. With advance information from the CBM system, spare parts can be ordered in time, so that they are available for maintenance when needed. Here, prognostics and CBM can improve the effectiveness and responsiveness of facility by improving visibility to expected demand throughout the supply chain.

The sixth is a leaner supply chain / inventory. A CBM system also provides important advantages to the supply chain. The advantages are a reduction in reliance on expensive modes of transportation to meet urgent demands, as well as a reduction in the quantity of spares in inventory. One of the potentially important impacts upon facility operations is the ability to provide advanced warning of an impending equipment failure through prognostics. Prognostics can also allow for decreased inventory throughout all stages of the supply chain because of earlier warning of parts failure. Parts can be ordered and received before repairs are necessary and there can be potential substantial savings in inventory investment.

The seventh is the estimation of remaining life of the equipment. One of the applications of the captured usage is the impact on the remaining usable life of the equipment, and hence the expected life of the facility as a whole. In a recent study, data deficiencies and access constraints in an organization wide system have been identified as making such analysis difficult though possible. Experience has shown that there is the need to understand the usage history of equipment. Also, with on-board systems, it allows detailed understanding of how usage and degradation is spread throughout the facility.

Possibly, one of the most significant positive impacts in implementing CBM strategies is that accurate, efficient, and effective information from failures and maintenance activities can enable informed and optimal management decision making. This normally results in a smaller footprint for the maintenance and operation support packages with more efficient maintenance, optimal system readiness, and cost savings.

CBM+ focuses on applying technology which improves maintenance capabilities and operational process, complements and improves reliability analysis efforts, involves the integration of support elements for enabling improved maintenance-centric management system response, and facilities more accurate predictions of impending failures, resulting in dramatic savings and improved facility availability. On the other hand, the most significant drawback of CBM can be on the initial bottom line. Costs associated with initial implementation of CBM / CBM+ strategies can be prohibitive to some programmes.

Implementing CBM techniques and strategies can mean new tools, test equipment, and / or embedded on-board diagnostics. Although, initially, maintenance and logistics costs can increase in the short-term (1 year to 2 year), if CBM / CBM+ is implemented correctly, the overall cost of the facility management, operations, and maintenance footprint is less than it has been without CBM / CBM+ strategies applied. However, this is a long-term goal in the normal short-term organizational budgets, developed one or two years (or months) at a time in response to yearly allocated funding levels. This makes the initial investment into CBM / CBM+ techniques less pleasant to managements. Even with organizational policies needing implementation of CBM / CBM+ strategies. CBM / CBM+ has to ‘buy its way’ into the programme.

CBM identifies maintenance actions based on near-real time assessment of equipment status from diagnostics sensors and equipment. Data collected from sensors, such as health and usage monitoring systems, are analyzed and translated into predictive trends or metrics which can anticipate when component failures occur and / or identify components which can need redesign or replacement for the reduction of the failure rates. If facilities need normal use of items and data on like-systems, the logistics footprints and costs get reduced considerably.

Another implementation challenge of CBM is to develop the basis of information, knowledge, and experience to help the programme and the management make the necessary changes for existing programmes and future acquisitions and system designs and development. Once established, however, a reliability or maintenance engineer can champion the CBM / CBM+ insertion in the design phase, recognize opportunities to modify operational systems when cost effective, and better communicate the operational readiness effects, maintenance benefits, and ROIs for justifying the cost of CBM+ implementation.

CBM implementation in operational facilities against new acquisition systems can pose substantial challenges. Particular issues include installation of on-line, or embedded, sensors, inadequate existing communications and data repository capabilities can hinder data collection and failure condition analysis, and off-line, or manual data gathering and analysis, capabilities cannot be as comprehensive as needed and can burden an already overworked maintenance work-force. There is a possibility that the operational data systems are not adequate.

When adding CBM to the existing systems, concentration is to be given on standardizing data management technologies by maximizing application of common health management software and standardized training. CBM / CBM+ is focused on determining the need for maintenance before operations are affected, and then having maintenance personnel respond to that need effectively and efficiently. When implementing CBM strategies, maintenance personnels are the key to the successful execution of equipment maintenance.

Maintenance personnels need to be well trained, well informed, well equipped, and well supported. This is where CBM+ exceeds the traditional CBM strategies, by ensuring that maintenance training is performed. Regardless of which maintenance and operational strategies are implemented, RCM, CBM, CBM+, the key to successful results are the maintenance personnel. Management can support the maintenance strategies, and maintenance engineers can recommend maintenance activities, procedures, and work flows, but without the buy-in of the maintenance personnel, none of these strategies and techniques are successful.

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