A Fuzzy System of Operation Safety Assessment Using Multimodel Linkage and Multistage Collaboration for In-Wheel Motor

Abstract

To simultaneously monitor some electrical or mechanical faults of an in-wheel motor and intelligently evaluate the operation safety, in this article, a fuzzy system of operation safety assessment (OSA) is proposed in this article. This method first uses many symptom parameters (SPs), such as root mean square, crest factor, temperature rise, and current covariance to express the features of the electrical and mechanical faults from different perspectives, such as vibration, noise, temperature, current, and voltage; possibility theory is employed to translate the probability density function of each SP into the possibility function, and sample data are gradually updated to optimize the possibility function for obtaining the SPs’ membership functions that are evaluation models. Second, the probabilities of the current operation state, that is safety, attention, or danger, are obtained from each evaluation model in a stage. Picture fuzzy set (PFS) is used to define a basic picture fuzzy number (PFN); thus, many PFNs from multiple models and multiple stages are used to establish an OSA's decision matrix. Third, Mahalanobis distance is reintegrated into PFS's theory for objectively judging the real-time evaluation information, and the best–worst method is used to estimate subjectively the initial evaluation experience; thus, the multimodel linkage mechanism is designed. Finally, TODIM is modified to define the relative safety ratio, and prospect theory is employed to structure the global index for formulating the multistage collaboration approach; thus, a fuzzy OSA's system is established. The effectiveness of the proposed method was verified by the experimental analysis for the operation safety of in-wheel motor with electrical and mechanical faults.