Trajectory Similarity Matching and Remaining Useful Life Prediction Based on Dynamic Time Warping
Algorithm 1
Trajectory similarity measurement algorithm.
Degradation trajectory similarity measurement based on DTW algorithm
Input: Test sample , degradation model
Output: Similarity between degradation trajectories
Process:
(1)
Take out the N-dimensional measurable monitoring data of a system, reduce the dimensions through PCA after normalization to obtain the characteristic vectors of the first M principal components , and utilize kernel regression to obtain the trajectory model for the test sample.
(2)
Using the same parameters for normalization, PCA, and kernel regression, process all the historical samples to obtain the system degradation trajectory model library .
(3)
For i = 1 to l (l = number of trajectories in the model library).
(4)
For j = 1 to k (k = number of trajectory points in the historical samples).
