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Full-Text Articles in Computer Engineering
Recent Advances And Trends Of Predictive Maintenance From Data-Driven Machine Prognostics Perspective, Yuxin Wen, Md. Fashiar Rahman, Honglun Xu, Tzu-Liang Bill Tseng
Recent Advances And Trends Of Predictive Maintenance From Data-Driven Machine Prognostics Perspective, Yuxin Wen, Md. Fashiar Rahman, Honglun Xu, Tzu-Liang Bill Tseng
Engineering Faculty Articles and Research
In the Engineering discipline, prognostics play an essential role in improving system safety, reliability and enabling predictive maintenance decision-making. Due to the adoption of emerging sensing techniques and big data analytics tools, data-driven prognostic approaches are gaining popularity. This paper aims to deliver an extensive review of recent advances and trends of data-driven machine prognostics, with a focus on their applications in practice. The primary purpose of this review is to categorize existing literature and report the latest research progress and directions to support researchers and practitioners in acquiring a clear comprehension of the subject area. This paper first summarizes …
Multiple-Phase Modeling Of Degradation Signal For Condition Monitoring And Remaining Useful Life Prediction, Yuxin Wen, Jianguo Wu, Yuan Yuan
Multiple-Phase Modeling Of Degradation Signal For Condition Monitoring And Remaining Useful Life Prediction, Yuxin Wen, Jianguo Wu, Yuan Yuan
Engineering Faculty Articles and Research
Remaining useful life prediction plays an important role in ensuring the safety, availability, and efficiency of various engineering systems. In this paper, we propose a flexible Bayesian multiple-phase modeling approach to characterize degradation signals for prognosis. The priors are specified with a novel stochastic process and the multiple-phase model is formulated to a novel state-space model to facilitate online monitoring and prediction. A particle filtering algorithm with stratified sampling and partial Gibbs resample-move strategy is developed for online model updating and residual life prediction. The advantages of the proposed method are demonstrated through extensive numerical studies and real case studies.