Acoustic emission sensor network based fault diagnosis of induction motors using a gabor filter and multiclass support vector machines

Abstract

Reliable and efficient fault diagnosis of induction motors is an important issue in industrial environments. This paper proposes a method for reliable fault diagnosis of induction motors using signal processing of acoustic emission (AE) data, including Gabor filtering and the use of multiclass support vector machines (MCSVMs), where a ZigBee based wireless sensor network (WSN) model is used for efficiently transmitting AE signals to a diagnosis server. In the proposed fault diagnosis approach, the induction motor’s different state signals are acquired through proper placement of AE sensors. The AE data are sent to a server through the wireless sensor network and decomposed using discrete wavelet transformation (DWT). An appropriate band is then selected using the maximum energy ratio, and a one-dimensional (1D) Gabor filter with various frequencies and orientation angles is applied to reduce abnormalities and extract various statistical parameters for generating features. In addition, principal component analysis (PCA) is applied to the extracted features to select the most dominant feature dimensions. Finally, one-against-one multiclass support vector machines (OAA-MCSVMs) are used to classify multiple fault types of an induction motor, where each SVM individually trains with its own features to increase the fault classification accuracy of the induction motor. In experiments, the proposed approach achieved an average classification accuracy of 99.80%, outperforming conventional fault diagnosis models.