A predictive maintenance system for multi-granularity faults based on AdaBelief-BP neural network and fuzzy decision making

Abstract

Predictive maintenance of production equipment is a prerequisite to ensure safe and reliable manufacturing operations. To avoid unexpected shutdown and even casualties caused by faults during production, it is paramount to design an effective predictive maintenance decision system for production equipment. Most of the related research works concentrate on early warn of specific faults but neglect the differentiations of the fault severity. To address the issue, this paper presents an intelligent predictive maintenance system for multi-granularity faults of production equipment based on the AdaBelief-BP (back propagation) neural network and the fuzzy decision making. The characteristics of the system presented in this paper include: (1) The proposed system implements a two-stage framework, integrating the functions of fault type prediction and fault degree prediction, which can provide comprehensive fault information throughout production lifecycles; (2) On the maintenance solution identification stage, fuzzy logic-based decision making is carried out to determine appropriate maintenance solutions based on the practical vague boundary of fault severity. In the system, the design of the AdaBelief-BP neural network can achieve a higher convergence rate and a better generalization capability as well. Meanwhile, to the best of our knowledge, in this research, it is the first time to use the migration of the fuzzy membership degree as the indicator of the changing condition of fault severity to facilitate more accurate maintenance solution identification. To verify the effectiveness of the system, comparison experiments with some popular algorithms are conducted. Benchmarking results show that the developed system can achieve higher prediction accuracy as well as higher efficiency than the comparative methods.