TR2021-107
Anomaly Detection and Diagnosis Using Pre-Processing and Time-Delay Autoencoder
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- "Anomaly Detection and Diagnosis Using Pre-Processing and Time-Delay Autoencoder", IEEE International conference on emerging technologies and factory automation, September 2021.BibTeX TR2021-107 PDF
- @inproceedings{Liu2021sep,
- author = {Liu, Bryan and Guo, Jianlin and Koike-Akino, Toshiaki and Wang, Ye and Kim, Kyeong Jin and Parsons, Kieran and Orlik, Philip V. and Hashimoto, Shigeru and Yuan, Jinhong},
- title = {Anomaly Detection and Diagnosis Using Pre-Processing and Time-Delay Autoencoder},
- booktitle = {IEEE International conference on emerging technologies and factory automation},
- year = 2021,
- month = sep,
- url = {https://www.merl.com/publications/TR2021-107}
- }
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- "Anomaly Detection and Diagnosis Using Pre-Processing and Time-Delay Autoencoder", IEEE International conference on emerging technologies and factory automation, September 2021.
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MERL Contacts:
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Research Areas:
Artificial Intelligence, Communications, Machine Learning, Signal Processing
Abstract:
This paper proposes an anomaly detection algorithm for a factory automation system, which jointly performs data pre-processing and time-delay autoencoder (TDAE) with a hybrid loss function. The source data are pre-processed by digital filters before feeding into a TDAE for anomaly detection. The digital filters extract analog signals from a variety of frequency bands to facilitate identifying anomalies. The pre-processed data then takes time-delay reform to explore temporal relationship of data signals. In addition, two anomaly diagnosis algorithms, a statistical based method and an autoencoder based method, are presented. Numerical results show that time-delay reform can improve the anomaly detection accuracy compared to the conventional autoencoder. Data pre-processing can further improve the anomaly detection accuracy. Moreover, we confirm that our anomaly diagnosis algorithms outperform traditional method that does not perform data pre-processing and time-delay reform.
Related News & Events
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NEWS Jianlin Guo recently delivered an invited talk at 2022 6th International Conference on Intelligent Manufacturing and Automation Engineering Date: December 15, 2022 - December 17, 2022
MERL Contacts: Jianlin Guo; Philip V. Orlik; Kieran Parsons
Research Areas: Artificial Intelligence, Data Analytics, Machine LearningBrief- The performance of manufacturing systems is heavily affected by downtime – the time period that the system halts production due to system failure, anomalous operation, or intrusion. Therefore, it is crucial to detect and diagnose anomalies to allow predictive maintenance or intrusion detection to reduce downtime. This talk, titled "Anomaly detection and diagnosis in manufacturing systems using autoencoder", focuses on tackling the challenges arising from predictive maintenance in manufacturing systems. It presents a structured autoencoder and a pre-processed autoencoder for accurate anomaly detection, as well as a statistical-based algorithm and an autoencoder-based algorithm for anomaly diagnosis.