Abstract
The fault prediction and abductive fault diagnosis of three-phase induction motors are of great importance for improving their working safety, reliability, and economy; however, it is difficult to succeed in solving these issues. This paper proposes a fault analysis method of motors based on modified fuzzy reasoning spiking neural P systems with real numbers (rMFRSNPSs) for fault prediction and abductive fault diagnosis. To achieve this goal, fault fuzzy production rules of three-phase induction motors are first proposed. Then, the rMFRSNPS is presented to model the rules, which provides an intuitive way for modelling the motors. Moreover, to realize the parallel data computing and information reasoning in the fault prediction and diagnosis process, three reasoning algorithms for the rMFRSNPS are proposed: the pulse value reasoning algorithm, the forward fault prediction reasoning algorithm, and the backward abductive fault diagnosis reasoning algorithm. Finally, some case studies are given, in order to verify the feasibility and effectiveness of the proposed method.
Highlights
As an important part of industrial and agricultural productions, the normal operation of three-phase induction motors plays a pivotal role in economic benefits and security risks
E fault prediction of a motor is usually realized based on an online monitoring system to detect the early failure symptoms and trend parameters that can reflect hidden troubles. en, these symptoms and parameters are processed by prediction algorithms to obtain early-warning information and integrated decision making [6] to prevent motor failures
Reference [8] proposed a two-stage machine learning analysis architecture, where a recurrent neural network-based variational autoencoder was proposed in the first stage, and principal components analysis and linear discriminant analysis techniques were applied in the second stage. is architecture was useful to accurately predict the motor fault modes only by using motor vibration time-domain signals
Summary
As an important part of industrial and agricultural productions, the normal operation of three-phase induction motors plays a pivotal role in economic benefits and security risks. In [14], an intelligent fault diagnosis of three-phase induction motors using a signal-based method was proposed and tested in different situations, in order to verify its availability in diagnosing failures, even when the operating mode data were limited. Erefore, to give full play to the excellent information processing ability and computing power of SNPSs, it is of great importance to expand their scope to different application fields, as well as extend the applications from the postante ones to new ex-ante analysis and prediction frameworks. Erefore, this paper moves forward in this widening of the scope of SNPSs. the work proposes a fault analysis method based on modified fuzzy reasoning spiking neural P systems with real numbers (rMFRSNPSs) for threephase induction motors. We extend its application from the postante diagnosis to a new ex-ante prediction framework. e new framework can take full advantages of the SNPS for the fault prediction with potential fault paths and their occurrence probabilities in an ex-ante prediction problem and can effectively find failure causes with abductive reasoning paths and their probabilities in a postante fault diagnosis problem
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