Unsupervised Dual Convolutional Autoencoder Models for Efficient Group Anomaly Detection of HST Bogies via Domain Adversarial Learning
The bogie system is known as the “legs” of high-speed train (HST), various failures will inevitably occur under large disturbances, high speeds, and heavy loads. Abnormal detection (AD) is an important means to detect the health status of its key components. Nevertheless, the cross-correlation of fa...
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| Published in: | Journal of physics. Conference series Vol. 2999; no. 1; pp. 12029 - 12035 |
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| Language: | English |
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| ISSN: | 1742-6588, 1742-6596 |
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| Abstract | The bogie system is known as the “legs” of high-speed train (HST), various failures will inevitably occur under large disturbances, high speeds, and heavy loads. Abnormal detection (AD) is an important means to detect the health status of its key components. Nevertheless, the cross-correlation of failures causes the confusion of health data and fault data under highly-coupled components, which leads to the issue of false detection and missing detection. Hence, this paper proposes a dual convolutional autoencoder (d-CAE) network combined with unsupervised domain-adversarial learning for group anomaly detection of bogies. Firstly, the d-CAE adopts temporal window aggregation to construct initial inputs. Afterwards, the domain-adversarial learning strategy is utilized to make the d-CAE realize multi-level encoding and reconstruction of multi-channel time-series. Finally, a parameterized dynamic AD index is designed to accurately establish the health sample guided abnormal decision boundary. The experimental results indicate that the d-CAE is competitive in the aspects of detection accuracy and robustness compared with the state-of-the-art methods. |
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| AbstractList | The bogie system is known as the “legs” of high-speed train (HST), various failures will inevitably occur under large disturbances, high speeds, and heavy loads. Abnormal detection (AD) is an important means to detect the health status of its key components. Nevertheless, the cross-correlation of failures causes the confusion of health data and fault data under highly-coupled components, which leads to the issue of false detection and missing detection. Hence, this paper proposes a dual convolutional autoencoder (d-CAE) network combined with unsupervised domain-adversarial learning for group anomaly detection of bogies. Firstly, the d-CAE adopts temporal window aggregation to construct initial inputs. Afterwards, the domain-adversarial learning strategy is utilized to make the d-CAE realize multi-level encoding and reconstruction of multi-channel time-series. Finally, a parameterized dynamic AD index is designed to accurately establish the health sample guided abnormal decision boundary. The experimental results indicate that the d-CAE is competitive in the aspects of detection accuracy and robustness compared with the state-of-the-art methods. |
| Author | Zhong, Shuncong Chang, Yuanhong Pan, Tongyang Xie, Jingsong |
| Author_xml | – sequence: 1 givenname: Yuanhong surname: Chang fullname: Chang, Yuanhong organization: Fujian Provincial Key Laboratory of Terahertz Functional Devices and Intelligent Sensing , 350108 Fuzhou, Fujian, China – sequence: 2 givenname: Shuncong surname: Zhong fullname: Zhong, Shuncong organization: Fujian Provincial Key Laboratory of Terahertz Functional Devices and Intelligent Sensing , 350108 Fuzhou, Fujian, China – sequence: 3 givenname: Tongyang surname: Pan fullname: Pan, Tongyang organization: Central South University School of Traffic & Transportation Engineering, 410083 Changsha, Hunan, China – sequence: 4 givenname: Jingsong surname: Xie fullname: Xie, Jingsong organization: Central South University School of Traffic & Transportation Engineering, 410083 Changsha, Hunan, China |
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| Cites_doi | 10.1016/j.patcog.2016.03.028 10.1016/j.iot.2022.100568 10.1016/j.isatra.2022.07.014 10.1016/j.jsv.2018.12.033 10.1109/ICDM.2008.17 10.1016/j.mechmachtheory.2020.104215 10.1109/ICDMW.2019.00152 10.1016/j.measurement.2022.112171 |
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| Title | Unsupervised Dual Convolutional Autoencoder Models for Efficient Group Anomaly Detection of HST Bogies via Domain Adversarial Learning |
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