Fault Diagnosis of Motor Bearing Based on Current Bi-Spectrum and Convolutional Neural Network

Abstract

Motor bearings are prone to different degrees of performance degradation, fatigue damage and failure undergoing complex and harsh environments. Vibration signal analysis is a mature method for diagnosing motor bearing faults, while it is not applicable for installing additional vibration sensors on many occasions. Practically, the fault of motor bearings changes the air gap flux between the rotor and stator, which leads to harmonic fluctuations in the stator current. The current signals can be used to diagnose the motor bearing faults without additional sensors. Inevitably the harmonics caused by the motor bearing faults will be coupled with the original signals. This paper combines bi-spectrum and Convolution Neural Network (CNN) to analyze the current signals of motor bearing faults. The CNN diagnosis model is trained based on the local bi-spectrum of current, and the CNN parameters are optimized. Diagnose and analyze motor bearing faults with different fault implantation methods, working conditions, fault degrees and fault locations. The diagnostic accuracy reaches more than 80%.

Research on convolutional neural networks in motor fault diagnosis

“…The aim was to find a suitable global context module that would enhance focus on global discriminative features within the model. Using a combination of bi-spectrum and CNN, Ma et al [8] investigated the current signals pertaining to motor bearing faults. A model known as the noise enhanced CNN (NBCNN) was developed by Chen et al [9] to effectively and precisely diagnose motor faults.…”