Mechanical faults detection for vehicle motors under nonstationary conditions based on Vold-Kalman order tracking method

Abstract

The fault diagnosis and health management of motors are the key technology that needs to be resolved in the future with the development of electric vehicles. The drive motors usually working under nonstationary conditions. Moreover, the mechanical fault is the most common type among the motor fault types. Therefore, developing the mechanical fault detection method under variable speed conditions would be of great value to improve the diagnosis accuracy of vehicle motors. This paper deals with the mechanism of the bearing fault, the rotor eccentricity fault, and their compound fault of permanent magnet synchronous motors. The purpose of this paper is to propose a mechanical fault detection method based on Vold-Kalman for vehicle motors under variable working conditions. First, the vibration characteristics of the healthy and the faulty motors are analyzed. Next, for eliminating the influence of variable conditions on the mechanical-fault detection, the fault characteristics are extracted by applying the Vold-Kalman filter. Finally, experiments are carried out based on the selected part of the new European driving cycle test conditions. The experimental results show that the proposed approach can detect the mechanical fault of the motor under nonstationary conditions effectively.

Bearings fault diagnosis under variable speed conditions by hypothesis-based FRFT technique

“…The other method needs to normalize the signal, because there are different dimensions and scales when displayed in the time and frequency domains [26,27]. The discrete scale normalization method is an effective dimensional normalization technique that performs scale stretching transformations on time series, making discrete data equivalent to the result of sampling after dimensional normalization of continuous signals.…”