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DCNN-Based Multi-Signal Induction Motor Fault Diagnosis

Shao, Siyu ; Yan, Ruqiang ; Lu, Yadong ; Wang, Peng ; Gao, Robert X.

IEEE transactions on instrumentation and measurement, 2020-06, Vol.69 (6), p.2658-2669 [Periódico revisado por pares]

New York: IEEE

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  • Título:
    DCNN-Based Multi-Signal Induction Motor Fault Diagnosis
  • Autor: Shao, Siyu ; Yan, Ruqiang ; Lu, Yadong ; Wang, Peng ; Gao, Robert X.
  • Assuntos: Architecture ; Artificial neural networks ; Computer architecture ; Computer simulation ; Convolutional neural network (CNN) ; Convolutional neural networks ; Deep learning ; deep learning (DL) ; Fault diagnosis ; Feature extraction ; Frequency distribution ; induction motor ; Induction motors ; Machine learning ; multi-signal model ; Representations ; Time-frequency analysis ; Vibration analysis ; Wavelet transforms
  • É parte de: IEEE transactions on instrumentation and measurement, 2020-06, Vol.69 (6), p.2658-2669
  • Descrição: 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.
  • Editor: New York: IEEE
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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