Degradation mode mining for complex system based on multi-sensor data fusion

Degenerate mode mining is of great significance to prediction of residual life of a complex system. Here, to understand operating state of the system and master its degradation law, a degradation mode mining method based on time series clustering was proposed. Firstly, the improved Mahalanobis-Taguchi system (MTS) was used to screen and fuse multi-sensor data features, and construct the health index for characterizing degradation trend of the system. Then, the health curve was segmented by using the cumulative sum algorithm to obtain degradation curve, and the hierarchical clustering algorithm based on dynamic time warping distance measurement was used to classify degradation modes. Finally, based on the similarity and degradation time, the degradation mode of the system was effectively identified. The aeroengine study showed that the proposed method can effectively mine and identify degradation modes, and provide a basis for predicting residual life of complex system. © 2022 Chinese Vibration Engineering Society. All rights reserved.