یک چارچوب برای مدل سازی نگهداری و تعمیرات پیشگویانه عملی برای سیستم های چند حالت

A simple practical framework for predictive maintenance (PdM)-based scheduling of multi-state systems (MSS) is developed. The maintenance schedules are derived from a system-perspective using the failure times of the overall system as estimated from its performance degradation trends. The system analyzed in this work is a flow transmission water pipe system. The various factors influencing PdM-based scheduling are identified and their impact on the system reliability and performance are quantitatively studied. The estimated times to replacement of the MSS may also be derived from the developed model. The results of the model simulation demonstrate the significant impact of maintenance quality and the criteria for the call for maintenance (user demand) on the system reliability and mean performance characteristics. A slight improvement in maintenance quality is found to postpone the system replacement time by manifold. The consistency in the quality of maintenance work with minimal variance is also identified as a very important factor that enhances the system's future operational and downtime event predictability. The studies also reveal that in order to reduce the frequency of maintenance actions, it is necessary to lower the minimum user demand from the system if possible, ensuring at the same time that the system still performs its intended function effectively. The model proposed can be utilized to implement a PdM program in the industry with a few modifications to suit the individual industrial systems’ needs.

Maintenance has evolved from the age-old ad hoc corrective (or reactive) maintenance [1] (CM) to preventive maintenance (PM) [2] and then to the presently popular predictive maintenance (PdM) [3] and [4] because both the CM and PM are well recognized as ineffective. In the case of CM, the “completely failed” system is highly degraded, making maintenance very difficult, time-consuming and expensive. Also, CM is associated with large and unpredictable downtimes resulting in low mean availability and increased forgone production losses. As for PM, the fixed downtime intervals would imply more-than-necessary repair frequency during the initial periods of the system operation that could increase the probability of maintenance-induced failures. On the other hand, as the system ages and enters into its wear-out period, PM results in less-than-necessary repair frequency, thereby increasing the probability of unanticipated catastrophic failures and making PM similar to CM. In PdM, which is also referred to as a condition-based PM [5], the maintenance schedule and frequency match the age or health of the system at all times, making the schedule nearly optimum, prolonging the time to replacement (TTR) as a consequence. The expected times to future failure of a system are estimated during each operational period based on the variation pattern of its physical properties (condition monitoring) that are indicative of its state of degradation using implanted sensors, and the downtime schedule for each operation cycle is determined based on the estimated future failure times. Past research studies show that the average system reliability (and yield), availability and mean system performance are the highest for PdM and the incurred maintenance operation costs are the lowest [6]. The spare part requirements and delay times are also reduced due to reliable prior predictions of future downtime events. However, there are currently two main obstacles to the practical implementation of the PdM policy. Firstly, there is no simple concrete statistical model that PdM can be based upon. The past models developed are theoretical in their approach with idealistic assumptions and fitting parameters, rendering them unfit for practical real-world implementation. For example, in [7], it was proposed that the system being repaired could be restored to either the “as-good-as-new” condition or the “as-bad-as-old” condition with complementary probabilities, failing to account for the possibility that the system's restoration could be somewhere in between these two possible extreme cases. Although the virtual age model proposed by Kijima [8] to account for the imperfect restoration helped overcome the above-mentioned problem, the determination of the effective age parameter “a” in the proposed model is not given, making its implementation vague. Secondly, the implementation of PdM requires advanced monitoring technologies, real-time data acquisition systems with sophisticated data storage and speed requirements and signal processing techniques [9], making the implementation of PdM complex and expensive. However, with the advances in sensor technologies today, this difficulty is gradually overcome [10]. In this work, we will focus on the first obstacle which is to develop a comprehensive and practical statistical model for PdM. Imperfect maintenance will be considered in this work for practical applications. This imperfect maintenance is a term frequently used to refer to maintenance activities in which the future reliability and degradation trend of the system depends on the skill and quality of the current and previous repair works performed. In other words, imperfect maintenance accounts for the impact of maintenance quality, due to maintenance personnel skill and spare part quality, on the future reliability of repairable systems. The structure of this paper is as follows. Section 2 gives a brief review on the various existing models for imperfect maintenance. Section 3 introduces the methodology for the multi-state systems (MSS) PdM modeling and the description of the system case study. Section 4 describes the various results from the model simulation and Section 5 discusses the limitations to be overcome. Finally, a short summary of the work done and results achieved is presented in Section 6.

A statistical model for the PdM policy of MSS-based systems has been developed by combining the UGF and Markov Chain analysis theories. RF, which is indicative of maintenance work quality and user demand (W), which represents minimum user expectations were identified as important PdM parameters and their impacts on the system performance, downtime schedule and replacement time was quantitatively examined. Using the stochastic model for the RF, system performance variation trends for various μRF, σRF and W values were simulated and presented graphically. The results clearly indicate the significant impact of μRF, σRF and W on system reliability. A highly skilled maintenance crew (high μRF) can help improve the system reliability and maintainability to a large extent, thus saving costs and reducing wear and tear of the system and in turn prolonging its useful lifespan. Consistent performance of maintenance (low σRF) is also very essential for more accurate predictability of future downtime schedules and times to system replacement (TTR) which in turn assist the management to precisely pre-plan the production activities so as to meet the timely customer market demands. Throughout this study, the model developed and the results shown were all based on the case study of a simple 3-element flow transmission water pipe MSS. However, it is important to take note that the exact same procedure described in this work could be applied to any generic n-element MSS of any type (flow transmission or task processing) with any arbitrary topology to construct the PdM model regardless of the system complexity. The only feature to take note of is that the system structure function, GS=ϕ(G1,G2,…,Gn), will vary for every system depending on its MSS classification and its topology [30]. Therefore, the model and results prescribed in this study are not just confined to the 3-element pipe system examined, but applicable in general to all operating systems in the industry. A company's long-term financial position hinges largely on its ability to reduce plant operational and maintenance costs, which currently accounts for as much as around 15–70% of its overall production expenses [1]. Maintenance cost reductions to lower levels can be partly achieved by implementing the new PdM policy proposed in this study and ensuring continuous sustained improvements in μRF and σRF.