Trajectory Similarity Matching and Remaining Useful Life Prediction Based on Dynamic Time Warping

Abstract

Remaining useful life prediction based on trajectory similarity is a typical example of instance-based learning. Hence, trajectory similarity prediction based on Euclidean distance has the problems of matching and low prediction accuracy. Therefore, an engine remaining useful life (RUL) prediction method based on dynamic time warping (DTW) is proposed. First, aiming at the problem of engine structure complexity and multiple monitoring parameters, the principal component analysis is used to reduce the dimension of multisensor signals. Then, the system performance degradation trajectory is extracted based on kernel regression. After obtaining the degradation trajectory database, the similarity matching of the degradation trajectory is carried out based on DTW. After finding the best matching curve, the RUL can be predicted. Finally, the proposed method is verified by the public aeroengine simulation dataset of NASA, and compared with several representatives and high-precision literature methods based on the same dataset, which verifies the effectiveness of the method.

Time Series Feature Selection Method Based on Mutual Information

“…For monitoring and control systems, in most cases, the monotonicity, predictability, and irreversibility of a single sensor signal cannot be guaranteed, nor can it fully reflect the system operability. Therefore, this paper uses the sensor information fusion method based on PCA dimension reduction and kernel smoothing to extract the system target variables, thus ensuring the monotonicity and irreversibility of the degradation trajectory [48].…”