Deep learning (DL) architecture, which exploits multiple hidden layers to learn hierarchical representations automatically from massive input data, presents a promising tool for characterizing fault conditions. This paper proposes a DL-based multi-signal fault diagnosis method that leverages the powerful feature learning ability of a convolutional neural network (CNN) in images. The proposed deep model is able to learn from multiple types of sensor signals simultaneously so that it can achieve robust performance and finally realize accurate induction motor fault recognition. First, the acquired sensor signals are converted to time-frequency distribution (TFD) by wavelet transform. Then, a deep CNN is applied to learning discriminative representations from the TFD images. Since then, a fully connected layer in deep architecture gives the prediction of induction motor condition based on learned features. In order to verify the effectiveness of the designed deep model, experiments are carried out on a machine fault simulator where both vibration and current signals are analyzed. Experimental results indicate that the proposed method outperforms traditional fault diagnosis methods, hence, demonstrating effectiveness in induction motor application. Compared with conventional methods that rely on delicate features extracted by experienced experts, the proposed deep model is able to automatically learn and select suitable features that contribute to accurate fault diagnosis. Compared with single-signal input, the multi-signal model has more accurate and stable performance and overcomes the overfitting problem to some degree.
|Number of pages||12|
|Journal||IEEE Transactions on Instrumentation and Measurement|
|State||Published - Jun 2020|
Bibliographical noteFunding Information:
Manuscript received February 27, 2019; revised May 6, 2019; accepted June 16, 2019. Date of publication July 1, 2019; date of current version May 12, 2020. This work was supported in part by the National Natural Science Foundation of China under Grant 51575102. The Associate Editor coordinating the review process was Edoardo Fiorucci. (Corresponding author: Ruqiang Yan.) S. Shao is with the School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China (e-mail: email@example.com).
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- Convolutional neural network (CNN)
- deep learning (DL)
- fault diagnosis
- induction motor
- multi-signal model
ASJC Scopus subject areas
- Electrical and Electronic Engineering