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Abnormal Detection for Running State of Linear Motor Feeding System Based on Deep Neural Networks

Author

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  • Zeqing Yang

    (School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China
    Key Laboratory of Hebei Province on Scale-Span Intelligent Equipment Technology, Tianjin 300130, China)

  • Wenbo Zhang

    (School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China)

  • Wei Cui

    (School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China)

  • Lingxiao Gao

    (School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China
    Key Laboratory of Hebei Province on Scale-Span Intelligent Equipment Technology, Tianjin 300130, China)

  • Yingshu Chen

    (School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China)

  • Qiang Wei

    (School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China)

  • Libing Liu

    (School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China
    Key Laboratory of Hebei Province on Scale-Span Intelligent Equipment Technology, Tianjin 300130, China)

Abstract

Because the linear motor feeding system always runs in complex working conditions for a long time, its performance and state transition have great randomness. Therefore, abnormal detection is particularly significant for predictive maintenance to promptly discover the running state degradation trend. Aiming at the problem that the abnormal samples of linear motor feed system are few and the samples have time-series features, a method of abnormal operation state detection of a linear motor feed system based on normal sample training was proposed, named GANomaly-LSTM. The method constructs an encoding-decoding-reconstructed encoding network model. Firstly, the time-series features of vibration, current and composite data samples are extracted by the long short-term memory (LSTM) network; Secondly, the three-layer fully connected layer is employed to extract potential feature vectors; Finally, anomaly detection of the system is completed by comparing the potential feature vectors of the two encodings. An experimental platform of the X-Y two-axis linkage linear motor feeding system is built to verify the rationality of the proposed method. Compared with other classical methods such as GANomaly and GAN-AE, the average AUROC index of this method is improved by 17.5% and 9.3%, the average accuracy is enhanced by 11.6% and 15.5%, and the detection time is shortened by 223 ms and 284 ms, respectively. GANomaly-LSTM has successfully proved its superiority for abnormal detection for running state of linear motor feeding systems.

Suggested Citation

  • Zeqing Yang & Wenbo Zhang & Wei Cui & Lingxiao Gao & Yingshu Chen & Qiang Wei & Libing Liu, 2022. "Abnormal Detection for Running State of Linear Motor Feeding System Based on Deep Neural Networks," Energies, MDPI, vol. 15(15), pages 1-22, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:15:p:5671-:d:880539
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    References listed on IDEAS

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    1. Chen, Hansi & Liu, Hang & Chu, Xuening & Liu, Qingxiu & Xue, Deyi, 2021. "Anomaly detection and critical SCADA parameters identification for wind turbines based on LSTM-AE neural network," Renewable Energy, Elsevier, vol. 172(C), pages 829-840.
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