TY - JOUR
T1 - Degradation Data-Driven Time-To-Failure Prognostics Approach for Rolling Element Bearings in Electrical Machines
AU - Wu, Jun
AU - Wu, Chaoyong
AU - Cao, Shuai
AU - Or, Siu Wing
AU - Deng, Chao
AU - Shao, Xinyu
N1 - Funding Information:
Manuscript received September 7, 2017; revised December 24, 2017 and January 21, 2018; accepted February 11, 2018. Date of publication March 1, 2018; date of current version August 31, 2018. This work was supported in part by the National Natural Science Foundation of China under Grant 51475189 and Grant 51721092, in part by the Foundation of the National Key Intergovernmental Special Project Development Plan of China under Grant 2016YFE0121700, in part by the Fundamental Research Funds for the Central Universities under Grant 2016YXMS050, and in part by the Innovation and Technology Commission of the HKSAR Government to the Hong Kong Branch of National Rail Transit Electrification and Automation Engineering Technology Research Center under Grant 1-BBYW. (Corresponding authors: Jun Wu and Xinyu Shao.) J. Wu, C. Wu, and S. Cao are with the School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan 430074, China, and also with the Collaborative Innovation Center for Advanced Ship and Deep-Sea Exploration, Wuhan 430074, China (e-mail: [email protected]; [email protected]; [email protected]).
Publisher Copyright:
© 1982-2012 IEEE.
PY - 2019/1
Y1 - 2019/1
N2 - Time-to-failure (TTF) prognostic plays a crucial role in predicting remaining lifetime of electrical machines for improving machinery health management. This paper presents a novel three-step degradation data-driven TTF prognostics approach for rolling element bearings (REBs) in electrical machines. In the degradation feature extraction step, multiple degradation features, including statistical features, intrinsic energy features, and fault frequency features, are extracted to detect the degradation phenomenon of REBs using complete ensemble empirical mode decomposition with adaptive noise and Hilbert-Huang transform methods. In degradation feature reduction step, the degradation features, which are monotonic, robust, and correlative to the fault evolution of the REBs, are selected and fused into a principal component Mahalanobis distance health index using dynamic principal component analysis and Mahalanobis distance methods. In TTF prediction step, the degradation process and local TTF of the REBs are observed by an exponential regression-based local degradation model, and the global TTF is predicted by an empirical Bayesian algorithm with a continuous update. A practical case study involving run-to-failure experiments of REBs on PRONOSTIA platform is provided to validate the effectiveness of the proposed approach and to show a more accurate prediction of TTF than the existing major approaches.
AB - Time-to-failure (TTF) prognostic plays a crucial role in predicting remaining lifetime of electrical machines for improving machinery health management. This paper presents a novel three-step degradation data-driven TTF prognostics approach for rolling element bearings (REBs) in electrical machines. In the degradation feature extraction step, multiple degradation features, including statistical features, intrinsic energy features, and fault frequency features, are extracted to detect the degradation phenomenon of REBs using complete ensemble empirical mode decomposition with adaptive noise and Hilbert-Huang transform methods. In degradation feature reduction step, the degradation features, which are monotonic, robust, and correlative to the fault evolution of the REBs, are selected and fused into a principal component Mahalanobis distance health index using dynamic principal component analysis and Mahalanobis distance methods. In TTF prediction step, the degradation process and local TTF of the REBs are observed by an exponential regression-based local degradation model, and the global TTF is predicted by an empirical Bayesian algorithm with a continuous update. A practical case study involving run-to-failure experiments of REBs on PRONOSTIA platform is provided to validate the effectiveness of the proposed approach and to show a more accurate prediction of TTF than the existing major approaches.
KW - Degradation data-driven approach
KW - degradation feature
KW - electrical machines
KW - rolling element bearings (REBs)
KW - time-to-failure (TTF) prognostics
UR - http://www.scopus.com/inward/record.url?scp=85042856837&partnerID=8YFLogxK
U2 - 10.1109/TIE.2018.2811366
DO - 10.1109/TIE.2018.2811366
M3 - Journal article
AN - SCOPUS:85042856837
SN - 0278-0046
VL - 66
SP - 529
EP - 539
JO - IEEE Transactions on Industrial Electronics
JF - IEEE Transactions on Industrial Electronics
IS - 1
M1 - 8305645
ER -