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Anomaly Detection with GRU Based Bi-autoencoder for Industrial Multimode Process
Xu, Xinyao1,2; Qin, Fangbo1,2; Zhao, Wenjun3; Xu, De1,2; Wang, Xingang1,2; Yang, Xihao3
Source PublicationINTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
ISSN1598-6446
2022-04-29
Pages14
Corresponding AuthorXu, Xinyao(xuxinyao2018@ia.ac.cn)
AbstractThe 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.
KeywordAnomaly detection autoencoder gated recurrent unit multimode process
DOI10.1007/s12555-021-0323-6
WOS KeywordMULTIPLE OPERATING MODES ; FAULT-DETECTION ; NETWORK ; PCA
Indexed BySCI
Language英语
Funding ProjectNational Key R&D Program of China[2018YFD0400902] ; State Key Laboratory of Smart Manufacturing for Special Vehicles and Transmission System[GZ2019KF008]
Funding OrganizationNational Key R&D Program of China ; State Key Laboratory of Smart Manufacturing for Special Vehicles and Transmission System
WOS Research AreaAutomation & Control Systems
WOS SubjectAutomation & Control Systems
WOS IDWOS:000788953900005
PublisherINST CONTROL ROBOTICS & SYSTEMS, KOREAN INST ELECTRICAL ENGINEERS
Sub direction classification目标检测、跟踪与识别
Citation statistics
Cited Times:6[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/48426
Collection精密感知与控制研究中心_精密感知与控制
Corresponding AuthorXu, Xinyao
Affiliation1.Chinese Acad Sci, Res Ctr Precis Sensing & Control, Inst Automat, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China
3.State Key Lab Smart Mfg Special Vehicles & Transm, Baotou City 014000, Inner Mongolia, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Xu, Xinyao,Qin, Fangbo,Zhao, Wenjun,et al. Anomaly Detection with GRU Based Bi-autoencoder for Industrial Multimode Process[J]. INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS,2022:14.
APA Xu, Xinyao,Qin, Fangbo,Zhao, Wenjun,Xu, De,Wang, Xingang,&Yang, Xihao.(2022).Anomaly Detection with GRU Based Bi-autoencoder for Industrial Multimode Process.INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS,14.
MLA Xu, Xinyao,et al."Anomaly Detection with GRU Based Bi-autoencoder for Industrial Multimode Process".INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS (2022):14.
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