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

Zeqing Yang, Wenbo Zhang, Wei Cui, Lingxiao Gao, Yingshu Chen, Qiang Wei and Libing Liu
Additional contact information
Zeqing Yang: School of Mechanical Engineering, Hebei University of 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
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

Energies, 2022, vol. 15, issue 15, 1-22

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.

Keywords: linear motor feeding system; lack of abnormal samples; deep neural network; anomaly detection; semi-supervised anomaly detection generative adversarial network (GANomaly); long short-term memory (LSTM) network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/15/5671/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/15/5671/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:15:p:5671-:d:880539

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().