Artificial Intelligence Enhanced Two-Stage Hybrid Fault Prognosis Methodology of PMSM

Fault prognosis based on single model is generally inaccurate due to the varying working conditions. A multistage fault prognosis methodology combining stage identification with Bayesian networks (BNs) and time series approach with particular emphasis on the autoregressive moving average (ARMA) model is proposed to solve this problem. In the first stage, degradation data are identified, and outliers are marked by the Euclidean distance. Degenerate attributes of outliers are finely identified by BNs and matched to the corresponding model. In the second stage, the ARMA model is used for prognosis according to the results of the fine identification. Subsequently, the double-precision identification and ARMA submodel prognosis are carried out alternately throughout the prognosis process. Three degradation types of permanent magnet synchronous motor are simulated to verify the applicability of the method. Result shows that it can track the changes in the degradation in time and obtains better results.

Automated summary

This paper proposes a multistage fault prognosis methodology that combines stage identification, Bayesian networks, and time series approach to address the issue of inaccurate fault prognosis based on a single model. The method achieves better results by accurately identifying and matching outliers, and using the ARMA model for prognosis.