Just-in-time learning based probabilistic gradient boosting tree for valve failure prognostics
— Historical failure instances of a system with diversified degradation patterns will pose great challenge for prognostics. Consequently, it is challenging to accurately predict the remaining useful life (RUL) using a prognostic model trained from such data. To solve this problem, this paper proposes a just-in-time learningbased data-driven prognostic method for reciprocating compressors with diverse degradation patterns and operating modes. The proposed framework employs a just-in-time learning (JITL) scheme to deal with the stochastic nature of fault evolution and the diversity of degradation patterns. Moreover, a data-driven forecasting model that features a randomized and smoothed gradient boosting decision tree (RS-GBDT) is developed for RUL and uncertainty predictions. The effectiveness of the proposed approach was validated on temperature measurements collected from 13 valve failure cases of an industrial reciprocating compressor.
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
Citation : Li, X., Mba, D.,Yang, Y., Loukopoulos, P. (2020) Just-in-time learning based probabilistic gradient boosting tree for valve failure prognostics. Mechanical Systems and Signal Processing, 150, 107253
ISSN : 0888-3270
Research Institute : Institute of Artificial Intelligence (IAI)