Track vibration sequence anomaly detection algorithm based on LSTM

Subway structure monitoring obtains structure monitoring data in real time, and the obtained subway track vibration sequence exhibits obvious time series characteristics. Therefore, the difficulties of abnormal detection of subway track vibration sequence include not only the general scarcity and diversity of data, but also the large amount of sample data. In this paper, an anomaly detection method based on the long short-term memory (LSTM) was applied to detect anomalous subway track vibration sequences. Firstly, subway track vibration signals were preprocessed. An approach to extract subway track vibration sequences was proposed. According to this method, whether the collected data constituted running data was determined via the mean square error, and track vibration sequences were then extracted from the original data according to an adaptive threshold value to obtain subway track vibration samples. Afterwards, Savitzky-Golay filtering was performed to smooth the obtained subway track vibration sequences, and then the wavelet transform was applied to denoise the signal. Second, an anomaly detection algorithm based on the LSTM was employed to detect subway track vibration sequences. Finally, compared to other algorithms, the LSTM algorithm performed better in anomaly detection on the subway track vibration dataset with a small anomaly proportion than did the other three methods. However, in the case of a large proportion of anomalies in the signal, the detection effect of the proposed algorithm was close to BPNN and superior to the LOF and OCSVM. The results indicated that the LSTM-based sequence anomaly detection algorithm attained a satisfactory detection effect for subway track vibration sequences. The anomaly detection algorithm can be applied to subway structure monitoring systems, which can monitor subway track vibration signals in real time and determine whether these signals are anomalous to ensure the safe operation of subway structures.