Fuzzy Logic Reliability Centered Maintenance

Abstract

Reliability Centered Maintenence (RCM) is a systematic maintenence strategy based on system reliability. Application of RCM process will not always come out with a binary output of "yes" and "no". Most of the time they are not supported with available detail information to calculate system reliability. The fuzzy logic method attempts to eliminate the uncertainty by providing "truth" in different degrees.Data and responses from maintenance department will be processed using the two methods (reliability centered maintenance and fuzzy logic) to design maintenance strategy for the company. The results of the fuzzy logic RCM application are maintenance strategy which fit with current and future condition.

Appropriate strategy selection for reliability-centered maintenance of one-shot systems using fuzzy model

“…The procedure adopted here is in line with a good number of other previous studies carried out in the field (e.g. Salehi, 2017;Baban et al, 2016;Felecia, 2014). In this study, however, possible strategies and input criteria are defined as per one-shot equipment.…”

“…Baban et al (2016) exploited a fuzzy expert system to forecast the proper maintenance procedure for a particular type of textile machinery. Felecia (2014) determined the best RCM policy by resorting to a fuzzy inference system. Having defined relevant indicators, the author of this study identifies the best strategy from among preventive maintenance (PM), corrective maintenance (CM), condition-based maintenance (CBM) and breakdown maintenance (BM) by employing if-then fuzzy rules predictions.…”

“…And so, these conditions can be decided by forming a fuzzy expert system (Nasruddin et al, 2017) and fuzzy inferences in order to determine the remaining life of such equipment (Hu et al, 2018) or the best maintenance policy (Eslami et al, 2014). Also, the approach is used to determine the RCM strategies (Felecia, 2014), seeming to be applicable in one-shot devices.…”

A systematic reliability-centred maintenance framework with fuzzy computational integration – a case study of manufacturing process machinery

“…This study used the fuzzy computational process to improve RPN estimation, resulting in a more accurate criticality ranking determination. Although many researchers have reported the integration of fuzzy computational processes in FMEA/FMECA to improve RPN estimation (Kumru and Kumru, 2013;Da gsuyu et al, 2016;Felecia, 2014;Gupta and Mishra, 2017;George et al, 2019;Ahmed and Gu, 2020;Buffa et al, 2020;Gupta et al, 2021), their studies were conducted and discussed in a specific scope of FMEA/FMECA application. In other words, the continuation processes of fuzzy RPN estimation to criticality ranking determination and Awad and As'ad (2016) presented a comprehensive RCM methodology that fundamentally used the same direction of idea as the RCM methodology proposed in this paper.…”

“…Although many researchers have reported the integration of fuzzy computational processes in FMEA/FMECA to improve RPN estimation (Kumru and Kumru, 2013; Dağsuyu et al. , 2016; Felecia, 2014; Gupta and Mishra, 2017; George et al. , 2019; Ahmed and Gu, 2020; Buffa et al.…”