Multisensor-Driven Motor Fault Diagnosis Method Based on Visual Features
Yao Tang, Xiaofei Zhang, Sheng Huang, Guojun Qin, Yunze He, Yinpeng Qu, Jinping Xie, Junhong Zhou, Zhuo Long
Abstract
Generalization ability is a critical property for practical motor fault diagnosis (FD). By converting time-series to images, several studies have made certain achievements. However, they still have following limitations. First, multisensor information fusion is rarely considered. Second, it is time consuming. To deal with the abovementioned problems, a multisensor-driven FD method based on visual features is proposed. Specifically, a color symmetrized dot pattern method is newly designed to infuse three multisensor signals to image. Next, a coarse and refined diagnosis framework is designed. In the coarse part, the color histogram features and a support vector machine (SVM) are utilized, and a threshold is selected to decide the coarse diagnostic samples. In the refined part, the gist (GIST) descriptor and another SVM are used to diagnose remaining samples. The results on induction motor and permanent magnet synchronous motor show that the proposed method achieved reliable diagnosis with relatively efficiency, and can generalize to different working conditions and noise.