- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
- Chinese S&T Journal Citation Reports
- Chinese Science Citation Database
| Citation: | ZHOU Xu, WEN Tao, LONG Zhiqiang. Anomaly Detection of Suspension System in Maglev Train Based on Missed Detection Rate[J]. Journal of Southwest Jiaotong University, 2023, 58(4): 903-912. doi: 10.3969/j.issn.0258-2724.20220770
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In order to detect data-driven anomalies of the suspension system in medium-speed maglev trains, firstly, the paper introduced an anomaly detection method based on parameterized residuals. Secondly, in response to the lack of prior information on anomalies in the current suspension system, the paper established a confidence set for the health data and anomaly data of the suspension system and determined the anomaly detection and evaluation function and threshold for the suspension system. Thirdly, to minimize the missed detection rate of anomalies at a fixed anomaly false alarm rate, the paper designed an optimal parameter vector from a mathematical perspective and constructed an anomaly detection algorithm for the suspension system based on the minimum missed detection rate. Finally, taking the operational data of the suspension system in the Changsha Maglev Express as an example, the paper detected the gap mutation anomaly, rail smashing anomaly, and acceleration sensor anomaly of the suspension system. The experimental results show that the proposed method can detect all three typical anomalies at a false alarm rate of 5%, without any missed detections of the three anomalies or false detections of normal data segments. The maximum anomaly detection delay is 0.2 s.
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