Fault Diagnosis Method of Electromagnetic Launch and Recovery Systems Based on Large-Scale Time Series Similarity Search

Rapid fault diagnosis of electromagnetic launch and recovery (EMLR) Systems is of great significance. It is an important research direction to diagnose the system fault by mining the event data collected during the operation of a certain type of arresting gear controller. While event data have the characteristics of high sampling rate, multidimensional coupling, and nonperiodic transient, which belongs to complex large-scale time series, the existing steady-state time series diagnosis methods are not applicable. In order to solve this problem, this article proposes a large-scale time series similarity search method based on Transformer and M-Tree index to realize fault diagnosis. First, the dimension of event data was preliminarily reduced by the proposed piecewise linear representation method integrating important points and piecewise aggregation approximation (PAA). Second, referring to Transformer and its variant models, a feature extraction model was constructed to further extract temporal features, and index was organized through M-Tree to improve search efficiency. Finally, an improved dynamic time warping (DTW) early abandon algorithm was proposed to further optimize the accurate calculation of DTW to quickly match and locate faults. Experimental results show that the proposed method can match working conditions, identify, and locate system abnormalities quickly and accurately, which proves the effectiveness of the algorithm.