A Novel Data-Driven Mechanical Fault Diagnosis Method for Induction Motors Using Stator Current Signals

This is the authors' abstract. We don't add key points for this paper.

Most of the mechanical fault diagnosis methods of induction motors (IMs) are based on vibration signals. However, vibration sensors are expensive and require direct contact with IMs. This article proposes a data-driven mechanical fault diagnosis method for IMs using stator current signals, which is more convenient and low cost, since no additional sensors are required. Aiming at the weak representation of mechanical faults in current signals, an intelligent noise elimination method based on noise reconstruction model is proposed to improve the signal-to-noise ratio. Through the automatic feature extraction and classification of the residual current envelope spectrum, high diagnosis accuracy can be obtained even if some differences have existed among samples. The effectiveness of the proposed method is verified by high accuracy diagnosis results on two experimental platforms. The results show that the average diagnostic accuracy of the proposed algorithm for one bearing fault and two eccentricity faults can reach 96%. Even if the fault type and the fault degree are distinguished at the same time, an accuracy of 90% can be achieved for six kinds of bearing faults.