Application of HMM in Fault Diagnosis of Permanent Magnet DC Motors

In order to solve the problem of fault diagnosis of permanent magnet DC (PMDC) motors, this paper applies Hidden Markov Model (HMM) to the fault identification and classification of motor. In this paper, based on the dynamic differential equation of the PMDC motors, the current simulation data of four motor states are obtained, including normal, inter-turn short circuit, brush wear and resistance increase. Then, the HMM is constructed, and the HMM is used to identify and classify the fault of the current simulation data. The results show that when the PMDC motors are faulty, its current signal characteristics will be abnormal, and the use of HMM to identify and classify the current signal of the motor has achieved good results, reflecting the advantages of HMM in the analysis of time-domain signals. The research results have certain reference value for the fault diagnosis of PMDC motors.