A Recurrent Neural Network Approach to Model Failure Rate Considering Random and Deteriorating Failures

Abstract

Recurrent neural networks (RNNs) utilize their internal state to handle variable length sequences, as time series; namely here as uncertain failure rates of the systems. Failure rate model of the components are required to improve systems reliability. Although the failure rate model has undeniable importance systems reliability assessment, an acceptable failure rate model has not been proposed to consider all causes of failures particularly random failures. Therefore, planners and decision makers are susceptible to a high financial risk for their decisions in the system. An approach is addressed to consider random failure rate along with deteriorating failure rate, to ameliorate this risks, in this paper. Therefore, the complexity of failure behavior is considered, while modeling considering the failure data as a time series. Moreover, the results of failure rate estimation are tested on a reliability-centered maintenance (RCM) implementation to prove the importance of random failure rate consideration. The results express that a more effective strategy can be regarded for preventive maintenance (PM) scheduling in RCM problem, when the proposed approach is utilized for failure rate modeling.