UM
Research of predicting machine's remaining useful life based on statistical pattern recognition and auto-regressive and moving average model
Liao W.-Z.2; Pan E.-S.1; Wang Y.1; Xi L.-F.1
2011-07-01
Source PublicationShanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University
ISSN10062467
Volume45Issue:7Pages:1000-1005
AbstractConsidering the important applications of predictive maintenance (PdM) today, it becomes essential to acquire machine's condition and its deterioration process. A machine's remaining useful life (RUL) model was proposed in which a statistical pattern recognition (SPR) method is developed to estimate machine's health index (HI) and an auto-regressive and moving average (ARMA) model is used to predict machine's RUL based on HI information, which greatly supports PdM planning. Through a case study, the computational results show that the proposed model is efficient and practical.
KeywordAuto-regressive and moving average (ARMA) model Health index (HI) Prediction Remaining useful life (RUL) Statistical pattern recognition (SPR)
URLView the original
Language英語
Fulltext Access
Document TypeJournal article
CollectionUniversity of Macau
Affiliation1.Shanghai Jiao Tong University
2.Chongqing University
Recommended Citation
GB/T 7714
Liao W.-Z.,Pan E.-S.,Wang Y.,et al. Research of predicting machine's remaining useful life based on statistical pattern recognition and auto-regressive and moving average model[J]. Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University,2011,45(7):1000-1005.
APA Liao W.-Z.,Pan E.-S.,Wang Y.,&Xi L.-F..(2011).Research of predicting machine's remaining useful life based on statistical pattern recognition and auto-regressive and moving average model.Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University,45(7),1000-1005.
MLA Liao W.-Z.,et al."Research of predicting machine's remaining useful life based on statistical pattern recognition and auto-regressive and moving average model".Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University 45.7(2011):1000-1005.
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