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R. Keith Mobley - One of the best experts on this subject based on the ideXlab platform.
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Predictive Maintenance Program
Rules of Thumb for Maintenance and Reliability Engineers, 2020Co-Authors: Ricky Smith, R. Keith MobleyAbstract:This chapter gives a complete overview of the Predictive Maintenance program. Predictive Maintenance is monitoring the vibration of rotating machinery in an attempt to detect incipient problems and prevent catastrophic failure. The common premise of Predictive Maintenance is that regular monitoring of the actual crafts condition, operating efficiency, and other indicators of operating condition of machine trains and process systems provide the data required to ensure the maximum interval between repairs and minimize the number and cost of unscheduled outages created by machine train failures. It is the means of improving productivity, product quality, and overall effectiveness of manufacturing and production plants. A comprehensive Predictive Maintenance management program uses a combination of the most cost effective tools, that is, vibration monitoring, thermography, tribology, and the like, to obtain the actual operating condition of critical plant systems and, based on this actual data, schedules all Maintenance activities on an “as needed” basis. Predictive Maintenance using vibration signature analysis is predicated on two basic facts: all common failure modes have distinct vibration frequency components that can be isolated and identified and the amplitude of each distinct vibration component remains constant unless there is a change in the operating dynamics of the machine train. A wide variety of Predictive techniques and technologies may provide benefit to a facility or plant. In most cases, more than one is needed for complete coverage of all critical assets and to gain maximum benefits from their use. In addition the chapter explains setting up a preventive/Predictive Maintenance program, visual inspection, vibration analysis, and many more concepts.
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Benefits of Predictive Maintenance
An Introduction to Predictive Maintenance, 2020Co-Authors: R. Keith MobleyAbstract:The premise of Predictive Maintenance is that regular monitoring of the actual mechanical condition of machine-trains and operating efficiency of process systems will ensure the maximum interval between repairs, minimize the number and cost of unscheduled outages created by machine-train failures, and improve the overall availability of operating plants. Including Predictive Maintenance in a total-plant management program will optimize the availability of process machinery and greatly reduce the cost of Maintenance. In reality, Predictive Maintenance is a condition-driven preventive Maintenance program. Predictive Maintenance is not a substitute for the more traditional Maintenance management methods. It is a valuable addition to a comprehensive, total plant Maintenance program. Where traditional Maintenance management programs rely on routine servicing of all machinery and fast response to unexpected failures, a Predictive Maintenance program schedules specific Maintenance tasks as they are actually required by plant equipment. It cannot eliminate the continued need for either or both of the traditional Maintenance programs. Predictive Maintenance can, however, reduce the number of unexpected failures and provide a more reliable scheduling tool for routine preventive Maintenance tasks.
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Establishing a Predictive Maintenance Program
An Introduction to Predictive Maintenance, 2020Co-Authors: R. Keith MobleyAbstract:Numerous Predictive Maintenance programs serve as models for implementing a successful Predictive Maintenance program. The decision to establish a Predictive Maintenance program is the first step toward controlling Maintenance costs and improving process efficiency in your plant. Unfortunately, many programs are aborted within the first three years, because a clear set of goals and objectives are not established before the program is implemented. Implementing a total-plant Predictive Maintenance program is expensive. After the initial capital cost of instrumentation and systems, a substantial annual labor cost is required to maintain the program. To be successful, a Predictive Maintenance program must be able to quantify the cost-benefit generated by the program. This goal can be achieved if the program is properly established, uses the proper Predictive Maintenance techniques, and has measurable benefits. The amount of effort expended to initially establish the program is directly proportional to its success or failure.
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A Total-Plant Predictive Maintenance Program
An Introduction to Predictive Maintenance, 2020Co-Authors: R. Keith MobleyAbstract:The selection of the best methods required to monitor the critical machines, equipment, and systems in a plant gets complicated with all of the techniques that are available for Predictive Maintenance. It would be convenient if a single system existed that would provide all of the monitoring and analysis techniques required to routinely monitor every critical piece of equipment. Unfortunately, this is not the case. Each of the Predictive techniques is highly specialized. Each has a group of systems vendors that promote their technique as the single solution to a plant's Predictive Maintenance needs. The result of this specialization is that no attempt has been made by Predictive Maintenance systems vendors to combine all of the different techniques into a single, total-plant system. Therefore, each plant must decide which combination of techniques and systems is required to implement its Predictive Maintenance program. The optimum Predictive Maintenance program consists of a combination of several monitoring techniques. Vibration technique is the primary method required to implement a total-plant program because most plants have large populations of mechanical systems.
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6 – Predictive Maintenance Techniques
An Introduction to Predictive Maintenance, 2002Co-Authors: R. Keith MobleyAbstract:This chapter provides a brief description of each of the techniques that should be included in a full-capabilities Predictive Maintenance program for typical plants. A variety of technologies are used as part of a comprehensive Predictive Maintenance program. Because mechanical systems, or machines, account for most plant equipment, vibration monitoring is generally the key component of most Predictive Maintenance programs. However, vibration monitoring cannot provide all of the information required for a successful Predictive Maintenance program. This technique is limited to monitoring the mechanical condition and not other critical parameters required to maintain reliability and efficiency of machinery. It is a very limited tool for monitoring critical process and machinery efficiencies and other parameters that can severely limit productivity and product quality. Therefore, a comprehensive Predictive Maintenance program must include other monitoring and diagnostic techniques. These techniques include vibration monitoring, thermography, tribology, process parameters, visual inspection, ultrasonics, and other nondestructive testing techniques.
S. Mondal - One of the best experts on this subject based on the ideXlab platform.
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Development of framework for Predictive Maintenance in Indian manufacturing sector
International Journal of Services and Operations Management, 2016Co-Authors: N. K. Srivastava, S. MondalAbstract:Every machine degrades with time and requires Maintenance. Among all types of Maintenance policies, Predictive Maintenance is established as the best form of Maintenance policy as numerous benefits are associated with it. Despite all the benefits, it finds restrictive usage in manufacturing companies. A literature survey reveals that limited funds is the major reason for restrictive usage of Predictive Maintenance, as Predictive Maintenance is capital intensive. In this paper, a Predictive Maintenance framework using Predictive Maintenance models with no investment on technology component is proposed for Indian manufacturing sector.
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Development of framework for Predictive Maintenance in Indian manufacturing sector
International Journal of Services and Operations Management, 2016Co-Authors: N. K. Srivastava, S. MondalAbstract:Every machine degrades with time and requires Maintenance. Among all types of Maintenance policies, Predictive Maintenance is established as the best form of Maintenance policy as numerous benefits are associated with it. Despite all the benefits, it finds restrictive usage in manufacturing companies. A literature survey reveals that limited funds is the major reason for restrictive usage of Predictive Maintenance, as Predictive Maintenance is capital intensive. In this paper, a Predictive Maintenance framework using Predictive Maintenance models with no investment on technology component is proposed for Indian manufacturing sector. Copyright © 2016 Inderscience Enterprises Ltd.
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Predictive Maintenance using modified FMECA method
International Journal of Productivity and Quality Management, 2015Co-Authors: N. K. Srivastava, S. MondalAbstract:This paper proposes a Predictive Maintenance policy using modified failure mode effect and criticality analysis (Mod-FMECA) technique. FMECA is used to identify failure modes, reasons, effects and criticality of the system (machine/plant) but in Mod-FMECA in addition to the analysis carried for FMECA, system performance in terms of output is measured and is used as an indicator to predict the failure mode of the system. The methodology so developed is validated using an example of coal pulverising mill. The proposed methodology helps in minimising the use of costly Predictive Maintenance equipments. However, further research on the proposed Predictive Maintenance policy may be carried out in order to extend its applicability to other industrial systems.
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Predictive Maintenance using modified FMECA method
International Journal of Productivity and Quality Management, 2015Co-Authors: N. K. Srivastava, S. MondalAbstract:Copyright © 2015 Inderscience Enterprises Ltd.This paper proposes a Predictive Maintenance policy using modified failure mode effect and criticality analysis (Mod-FMECA) technique. FMECA is used to identify failure modes, reasons, effects and criticality of the system (machine/plant) but in Mod-FMECA in addition to the analysis carried for FMECA, system performance in terms of output is measured and is used as an indicator to predict the failure mode of the system. The methodology so developed is validated using an example of coal pulverising mill. The proposed methodology helps in minimising the use of costly Predictive Maintenance equipments. However, further research on the proposed Predictive Maintenance policy may be carried out in order to extend its applicability to other industrial systems.
Navid Goudarzi - One of the best experts on this subject based on the ideXlab platform.
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ICPHM - PHM based Predictive Maintenance optimization for offshore wind farms
2015 IEEE Conference on Prognostics and Health Management (PHM), 2015Co-Authors: Peter Sandborn, Amir Kashani-pour, Roozbeh Bakhshi, Navid GoudarziAbstract:In this paper, a simulation-based real options analysis (ROA) approach is applied to valuate the Predictive Maintenance options created by PHM for multiple turbines in offshore wind farms managed under outcome-based contracts known as power purchase agreements (PPAs). When a remaining useful life (RUL) is predicted for a subsystem in a single turbine, a Predictive Maintenance option is triggered. If Predictive Maintenance is implemented before the subsystem or turbine fails, the option is exercised; if the Predictive Maintenance is not implemented and the subsystem or turbine runs to failure, the option expires and the option value is zero. The time-history cost avoidance and cumulative revenue paths are simulated considering the uncertainties in wind and the RUL predictions. By valuating a series of European real options based on all possible Predictive Maintenance opportunities, the Maintenance opportunity with the maximum value can be obtained. In a wind farm, there may be multiple turbines concurrently indicating RULs. To model multiple turbines managed via an outcome-based contract (PPA), the cumulative revenue and cost avoidance for each turbine depends on the operational state of the other turbines in the farm, the amount of energy that has been delivered and will be delivered by the whole farm. A case study is presented that determines the optimum Predictive Maintenance opportunity for a farm under a PPA, the optimum Predictive Maintenance opportunity for the same farm managed via an as-delivered contract, and the optimum Predictive Maintenance opportunities for individual turbines managed independently.
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PHM based Predictive Maintenance optimization for offshore wind farms
2015 IEEE Conference on Prognostics and Health Management: Enhancing Safety Efficiency Availability and Effectiveness of Systems Through PHAf Technolo, 2015Co-Authors: X. Lei, Amir Kashani-pour, Roozbeh Bakhshi, Peter Sandborn, Navid GoudarziAbstract:In this paper, a simulation-based real options analysis (ROA) approach is applied to valuate the Predictive Maintenance options created by PHM for multiple turbines in offshore wind farms managed under outcome-based contracts known as power purchase agreements (PPAs). When a remaining useful life (RUL) is predicted for a subsystem in a single turbine, a Predictive Maintenance option is triggered. If Predictive Maintenance is implemented before the subsystem or turbine fails, the option is exercised; if the Predictive Maintenance is not implemented and the subsystem or turbine runs to failure, the option expires and the option value is zero. The time-history cost avoidance and cumulative revenue paths are simulated considering the uncertainties in wind and the RUL predictions. By valuating a series of European real options based on all possible Predictive Maintenance opportunities, the Maintenance opportunity with the maximum value can be obtained. In a wind farm, there may be multiple turbines concurrently indicating RULs. To model multiple turbines managed via an outcome-based contract (PPA), the cumulative revenue and cost avoidance for each turbine depends on the operational state of the other turbines in the farm, the amount of energy that has been delivered and will be delivered by the whole farm. A case study is presented that determines the optimum Predictive Maintenance opportunity for a farm under a PPA, the optimum Predictive Maintenance opportunity for the same farm managed via an as-delivered contract, and the optimum Predictive Maintenance opportunities for individual turbines managed independently. © 2015 IEEE.
Guang Meng - One of the best experts on this subject based on the ideXlab platform.
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A Predictive Maintenance scheduling framework utilizing residual life prediction information
Proceedings of the Institution of Mechanical Engineers Part E: Journal of Process Mechanical Engineering, 2012Co-Authors: Guang MengAbstract:This article proposes a component-level Predictive Maintenance scheduling framework in which system residual life prediction information is utilized. In the proposed framework, Predictive Maintenance scheduling is only triggered when a system is close to failure. A lifetime margin is used to infer that a system is close to failure but still safely operational when Predictive Maintenance scheduling is triggered. Some technical elements are detailed to facilitate the implementation of the proposed framework. A simulation study based on an example Predictive Maintenance scheduling framework (framework B) is provided to illustrate the implementation of the proposed framework, and compare framework B with the corresponding counterpart only consisting of Predictive Maintenance scheduling models (framework A). Given a relatively low noise level of system degradation processes, compared with the classical prognostics framework only consisting of Predictive Maintenance scheduling models, the proposed framework is similarly effective in failure prevention and more economic in that several premature preventive Maintenance acts are saved.
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A modularized framework for Predictive Maintenance scheduling
Proceedings of the Institution of Mechanical Engineers Part O: Journal of Risk and Reliability, 2012Co-Authors: Ming-yi You, Guang MengAbstract:This paper presents a modularized, easy-to-implement framework for Predictive Maintenance scheduling. With a modularization treatment of a Maintenance scheduling model, a Predictive Maintenance scheduling model can be established by integrating components' real-time, sensory-updated prognostics information with a classical preventive Maintenance/condition-based Maintenance scheduling model. With the framework, a Predictive Maintenance scheduling model for multi-component systems is established to illustrate the framework's use; such a Predictive Maintenance scheduling model for multi-component systems has not been reported previously in the literature. A numerical example is provided to investigate the individual-orientation and dynamic updating characteristics of the optimal preventive Maintenance schedules of the established Predictive Maintenance scheduling model and to evaluate the performance of these preventive Maintenance schedules. It is hoped that the presented framework will facilitate the implementation of Predictive Maintenance policies in various industrial applications.
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A Predictive Maintenance scheduling framework utilizing residual life prediction information
Proceedings of the Institution of Mechanical Engineers Part E: Journal of Process Mechanical Engineering, 2012Co-Authors: Ming-yi You, Guang MengAbstract:This article proposes a component-level Predictive Maintenance scheduling framework in which system residual life prediction information is utilized. In the proposed framework, Predictive Maintenance scheduling is only triggered when a system is close to failure. A lifetime margin is used to infer that a system is close to failure but still safely operational when Predictive Maintenance scheduling is triggered. Some technical elements are detailed to facilitate the implementation of the proposed framework. A simulation study based on an example Predictive Maintenance scheduling framework (framework B) is provided to illustrate the implementation of the proposed framework, and compare framework B with the corresponding counterpart only consisting of Predictive Maintenance scheduling models (framework A). Given a relatively low noise level of system degradation processes, compared with the classical prognostics framework only consisting of Predictive Maintenance scheduling models, the proposed framework is similarly effective in failure prevention and more economic in that several premature preventive Maintenance acts are saved. © IMechE 2012 Reprints and permissions.
N. K. Srivastava - One of the best experts on this subject based on the ideXlab platform.
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Development of framework for Predictive Maintenance in Indian manufacturing sector
International Journal of Services and Operations Management, 2016Co-Authors: N. K. Srivastava, S. MondalAbstract:Every machine degrades with time and requires Maintenance. Among all types of Maintenance policies, Predictive Maintenance is established as the best form of Maintenance policy as numerous benefits are associated with it. Despite all the benefits, it finds restrictive usage in manufacturing companies. A literature survey reveals that limited funds is the major reason for restrictive usage of Predictive Maintenance, as Predictive Maintenance is capital intensive. In this paper, a Predictive Maintenance framework using Predictive Maintenance models with no investment on technology component is proposed for Indian manufacturing sector.
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Development of framework for Predictive Maintenance in Indian manufacturing sector
International Journal of Services and Operations Management, 2016Co-Authors: N. K. Srivastava, S. MondalAbstract:Every machine degrades with time and requires Maintenance. Among all types of Maintenance policies, Predictive Maintenance is established as the best form of Maintenance policy as numerous benefits are associated with it. Despite all the benefits, it finds restrictive usage in manufacturing companies. A literature survey reveals that limited funds is the major reason for restrictive usage of Predictive Maintenance, as Predictive Maintenance is capital intensive. In this paper, a Predictive Maintenance framework using Predictive Maintenance models with no investment on technology component is proposed for Indian manufacturing sector. Copyright © 2016 Inderscience Enterprises Ltd.
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Predictive Maintenance using modified FMECA method
International Journal of Productivity and Quality Management, 2015Co-Authors: N. K. Srivastava, S. MondalAbstract:This paper proposes a Predictive Maintenance policy using modified failure mode effect and criticality analysis (Mod-FMECA) technique. FMECA is used to identify failure modes, reasons, effects and criticality of the system (machine/plant) but in Mod-FMECA in addition to the analysis carried for FMECA, system performance in terms of output is measured and is used as an indicator to predict the failure mode of the system. The methodology so developed is validated using an example of coal pulverising mill. The proposed methodology helps in minimising the use of costly Predictive Maintenance equipments. However, further research on the proposed Predictive Maintenance policy may be carried out in order to extend its applicability to other industrial systems.
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Predictive Maintenance using modified FMECA method
International Journal of Productivity and Quality Management, 2015Co-Authors: N. K. Srivastava, S. MondalAbstract:Copyright © 2015 Inderscience Enterprises Ltd.This paper proposes a Predictive Maintenance policy using modified failure mode effect and criticality analysis (Mod-FMECA) technique. FMECA is used to identify failure modes, reasons, effects and criticality of the system (machine/plant) but in Mod-FMECA in addition to the analysis carried for FMECA, system performance in terms of output is measured and is used as an indicator to predict the failure mode of the system. The methodology so developed is validated using an example of coal pulverising mill. The proposed methodology helps in minimising the use of costly Predictive Maintenance equipments. However, further research on the proposed Predictive Maintenance policy may be carried out in order to extend its applicability to other industrial systems.
