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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| References | Huang (JPCS_2999_1_012029bib1) 2019; 444 Akcay (JPCS_2999_1_012029bib5) 2018 Yan (JPCS_2999_1_012029bib8) 2023; 133 Liu (JPCS_2999_1_012029bib7) 2008 Chatterjee (JPCS_2999_1_012029bib3) 2022; 19 Lu (JPCS_2999_1_012029bib2) 2021; 157 Erfani (JPCS_2999_1_012029bib6) 2016; 58 Zhang (JPCS_2999_1_012029bib9) 2022; 205 Luer (JPCS_2999_1_012029bib4) 2019 |
| References_xml | – volume: 58 start-page: 121 year: 2016 ident: JPCS_2999_1_012029bib6 article-title: High dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning[J] publication-title: Pattern Recognition doi: 10.1016/j.patcog.2016.03.028 – volume: 19 year: 2022 ident: JPCS_2999_1_012029bib3 article-title: IoT anomaly detection methods and applications: A survey[J] publication-title: Internet of Things doi: 10.1016/j.iot.2022.100568 – volume: 133 start-page: 53 year: 2023 ident: JPCS_2999_1_012029bib8 article-title: Memory-augmented skip-connected autoencoder for unsupervised anomaly detection of rocket engines with multi-source fusion[J] publication-title: ISA Transactions doi: 10.1016/j.isatra.2022.07.014 – volume: 444 start-page: 216 year: 2019 ident: JPCS_2999_1_012029bib1 article-title: A modified scale-space guiding variational mode decomposition for high-speed railway bearing fault diagnosis[J] publication-title: Journal of Sound and Vibration doi: 10.1016/j.jsv.2018.12.033 – year: 2008 ident: JPCS_2999_1_012029bib7 article-title: Isolation forest[C] doi: 10.1109/ICDM.2008.17 – volume: 157 year: 2021 ident: JPCS_2999_1_012029bib2 article-title: Coupling model and vibration simulations of railway vehicles and running gear bearings with multitype defects[J] publication-title: Mechanism and Machine Theory doi: 10.1016/j.mechmachtheory.2020.104215 – year: 2019 ident: JPCS_2999_1_012029bib4 article-title: Anomaly detection in time series using generative adversarial networks[C] doi: 10.1109/ICDMW.2019.00152 – year: 2018 ident: JPCS_2999_1_012029bib5 article-title: GANomaly: Semi-supervised anomaly detection via adversarial training[C] – volume: 205 year: 2022 ident: JPCS_2999_1_012029bib9 article-title: Retentive multimodal scale-variable anomaly detection framework with limited data groups for liquid rocket engine[J] publication-title: Measurement doi: 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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