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Remaining useful life prediction for non-Markovian degrading systems with multiple working conditions.

, , and . ICPHM, page 1-8. IEEE, (2018)

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ES-DLSSVM-Based Prognostics of Rolling Element Bearings., , , , and . IEEE Trans. Reliab., 73 (1): 317-327 (March 2024)Converting non-parametric distance-based classification to anytime algorithms., , , and . Pattern Anal. Appl., 11 (3-4): 321-336 (2008)Remaining useful life prediction for non-Markovian degrading systems with multiple working conditions., , and . ICPHM, page 1-8. IEEE, (2018)Supporting exact indexing of arbitrarily rotated shapes and periodic time series under Euclidean and warping distance measures., , , , , and . VLDB J., 18 (3): 611-630 (2009)A two-stage data-driven approach to remaining useful life prediction via long short-term memory networks., , and . Reliab. Eng. Syst. Saf., (September 2023)Remaining Useful Life Prediction for Hybrid Stochastic Degradation Processes with Finite Time Delay., , and . ECC, page 818-824. IEEE, (2020)Finding Motifs in a Database of Shapes., , , and . SDM, page 249-260. SIAM, (2007)Fast time series classification using numerosity reduction., , , , and . ICML, volume 148 of ACM International Conference Proceeding Series, page 1033-1040. ACM, (2006)Fast Best-Match Shape Searching in Rotation Invariant Metric Spaces., , , , and . SDM, page 611-616. SIAM, (2007)Remaining Useful Life Prediction for Degradation Processes With Memory Effects., , and . IEEE Trans. Reliability, 66 (3): 751-760 (2017)