Fuzzy reliability centered maintenance considering personnel experience and only censored data - Fuentes-Huerta, Marco A.; González-González, David S.; Cantú-Sifuentes, Mario; Praga-Alejo, Rolando J. (Computers & industrial engineering, 2021)
•The proposed maximum entropy model achieved 40% maintenance cost savings.•The fuzzy numbers provide support to the reliability estimation subject to uncertainty constraints.•The risk level (RPN) decreased by more than 70%. The automotive industry uses Reliability Centered Maintenance (RCM) to establish efficient and cost-effective maintenance schedules of equipment (IATF 16949). The RCM methodology is based on equipment reliability which requires collection of failure times. However, in some cases it is not possible to record enough information. Sometimes in the field, only service times are available (these can be classified as “only censored data”). The most commonly found situation waiting until the component fails. In that sense, the RCM methodology mainly uses the classic Weibull model for estimating reliability indexes, which cannot be performed if there are only censored data. The maximum entropy can be used to propose an alternative reliability model that is able to work under the limited information conditions. Nevertheless, the uncertainty of only censored data and the limited information also affect the maximum entropy model. The information available, such as maintenance staff experience, must help provide accuracy to reliability estimation, which can be addressed through fuzzy numbers. Hence, a fuzzy maximum entropy approach is proposed to determine the maximum entropy reliability function considering the uncertainty and the maintenance staff knowledge; therefore, the replacement frequency. Finally, the proposed method was applied to an injection molding machine. Outcomes proved that the alternative model is a useful and reliable option if there are only censored data and personnel experience as available information. The RCM maintenance plan was scheduled using the proposed methodology and its results showed 40% savings in the annual maintenance costs, which represent an important benefit for company.