Abstract
The anomaly detection for multimode industrial process is a challenging problem, because the multiple operation modes present various main distributions of monitored variables, and the dynamic sequential characteristics exist within each operation mode. This paper proposes an anomaly detection method based on sequence-to-sequence gated recurrent units (SGRU). First, to better model both the cross-mode trends and mode-specific sequential characteristics, a main reconstruction module and residual reconstruction module are integrated to improve the ability to represent complex process. Both modules are implemented by SGRUs. Second, a reconstruction error prediction module is designed to estimate the mean values of mode-specific reconstruction errors, which helps to determine the more reliable alarm thresholds. Third, the two anomaly indicators are utilized to represent the deviation degree of monitored variables against the normal conditions, according to the statistical errors and biases of reconstructions, respectively. The effectiveness of the proposed method is validated on simulations with multimode process, and on the practical data set collected from the Cleaning-in-Place multimode process of an aseptic beverage filling line in a real factory.
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This work was supported by National Key R&D Program of China (2018YFD0400902) and State Key Laboratory of Smart Manufacturing for Special Vehicles and Transmission System (GZ2019KF008).
Xinyao Xu received his B.Sc. degree in automation from Tianjin University, Tianjin, China, in 2018. He is currently pursuing a Ph.D. degree in control science and engineering at the Research Center of Precision Sensing and Control, Institute of Automation, Chinese Academy of Sciences (CASIA) and University of Chinese Academy of Sciences, Beijing, China. His research interests include industrial big data and fault diagnosis.
Fangbo Qin received his B.Sc. degree in automation from Beijing Jiaotong University, Beijing, China, in 2013. He received a Ph.D. degree in control science and engineering at CASIA and University of Chinese Academy of Sciences, Beijing, China, in 2019. He has been an Assistant Research Fellow at CASIA since 2019. His research interests include robot vision, robot manipulation, and deep learning.
Wenjun Zhao received his B.Sc. Degree in machinery manufacturing from Shenyang Ligong University, Shenyang, China, in 1985. He is currently a Senior Engineer at State Key Laboratory of Smart Manufacturing for Special Vehicles and Transmission System, Inner Mongolia First Machinery Group Co., Ltd. His research interests include digitization and intelligent manufacturing.
De Xu received his B.Sc. and M.Sc. degrees from the Shandong University of Technology, Jinan, China, in 1985 and 1990, respectively, and a Ph.D. degree from Zhejiang University, Hangzhou, China, in 2001, all in control science and engineering. He has been with CASIA since 2001. He is currently a Professor with the Research Center of Precision Sensing and Control, CASIA. His current research interests include robotics and automation such as visual measurement, visual control, intelligent control, welding seam tracking, visual positioning, microscopic vision, and micro-assembly.
Xingang Wang received his B.Sc. degree from Tianjin University, Tianjin, China, in 1995, and a Ph.D. degree from the Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, China, in 2002. He is currently a Professor with the Research Center of Precision Sensing and Control, CASIA, Beijing, China. His current research interests include image processing and machine learning.
Xihao Yang received his B.Sc. Degree in Mechanical Engineering and Automation from Jilin University, Changchun, China, in 2012. He is currently an engineer in State Key Laboratory of Smart Manufacturing for Special Vehicles and Transmission System, Inner Mongolia First Machinery Group Co., Ltd. His research interests include automation system and intelligent manufacturing.
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Xu, X., Qin, F., Zhao, W. et al. Anomaly Detection with GRU Based Bi-autoencoder for Industrial Multimode Process. Int. J. Control Autom. Syst. 20, 1827–1840 (2022). https://doi.org/10.1007/s12555-021-0323-6
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DOI: https://doi.org/10.1007/s12555-021-0323-6