Health Index-Based Prognostics for Remaining Useful Life Predictions in Electrical Machines. Yang, F., Habibullah, M. S., Zhang, T., Xu, Z., Lim, P., & Nadarajan, S. IEEE Transactions on Industrial Electronics, 63(4):2633–2644, April, 2016. Conference Name: IEEE Transactions on Industrial Electronicsdoi abstract bibtex Many industries have a growing awareness in utilizing new technologies to improve the reliability and availability of their systems. Prognostics, a subject concerned with the prediction of the remaining useful life (RUL), has been increasingly studied and applied to practical systems, such as electrical systems, over the past few years. Here, with the adoption of a data-driven prognostics framework, this paper proposed a health index (HI)-based prognostics method to predict the RUL of electrical machines. By assuming a linearly degrading HI over time, the proposed method predicts the RUL in two steps: 1) from input signals to HI; and then 2) mapping HI to RUL. The novelty of this method lies in the proposed dynamic HI smoothing approach where three characteristics of HI, namely monotonicity, gradualness, and consistency, are incorporated to smooth the current HI values with the previously predicted ones. Real data collected from eight electrical motors, subjected to accelerated thermal aging process, were used in the experimental studies, with the results showing the superiority of the proposed HI-based RUL prediction over the traditional direct RUL prediction (i.e., without HI).
@article{yang_health_2016,
title = {Health {Index}-{Based} {Prognostics} for {Remaining} {Useful} {Life} {Predictions} in {Electrical} {Machines}},
volume = {63},
issn = {1557-9948},
doi = {10.1109/TIE.2016.2515054},
abstract = {Many industries have a growing awareness in utilizing new technologies to improve the reliability and availability of their systems. Prognostics, a subject concerned with the prediction of the remaining useful life (RUL), has been increasingly studied and applied to practical systems, such as electrical systems, over the past few years. Here, with the adoption of a data-driven prognostics framework, this paper proposed a health index (HI)-based prognostics method to predict the RUL of electrical machines. By assuming a linearly degrading HI over time, the proposed method predicts the RUL in two steps: 1) from input signals to HI; and then 2) mapping HI to RUL. The novelty of this method lies in the proposed dynamic HI smoothing approach where three characteristics of HI, namely monotonicity, gradualness, and consistency, are incorporated to smooth the current HI values with the previously predicted ones. Real data collected from eight electrical motors, subjected to accelerated thermal aging process, were used in the experimental studies, with the results showing the superiority of the proposed HI-based RUL prediction over the traditional direct RUL prediction (i.e., without HI).},
number = {4},
journal = {IEEE Transactions on Industrial Electronics},
author = {Yang, Feng and Habibullah, Mohamed Salahuddin and Zhang, Tianyou and Xu, Zhao and Lim, Pin and Nadarajan, Sivakumar},
month = apr,
year = {2016},
note = {Conference Name: IEEE Transactions on Industrial Electronics},
keywords = {Aging, Data collection, Data models, Electrical Machine, Electrical machine, Feature extraction, Health Index, Hidden Markov models, Induction motors, Predictive models, Prognostics, Remaining Useful Life, health index (HI), prognostics, remaining useful life (RUL), sigkdd-rw},
pages = {2633--2644},
}
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By assuming a linearly degrading HI over time, the proposed method predicts the RUL in two steps: 1) from input signals to HI; and then 2) mapping HI to RUL. The novelty of this method lies in the proposed dynamic HI smoothing approach where three characteristics of HI, namely monotonicity, gradualness, and consistency, are incorporated to smooth the current HI values with the previously predicted ones. Real data collected from eight electrical motors, subjected to accelerated thermal aging process, were used in the experimental studies, with the results showing the superiority of the proposed HI-based RUL prediction over the traditional direct RUL prediction (i.e., without HI).","number":"4","journal":"IEEE Transactions on Industrial Electronics","author":[{"propositions":[],"lastnames":["Yang"],"firstnames":["Feng"],"suffixes":[]},{"propositions":[],"lastnames":["Habibullah"],"firstnames":["Mohamed","Salahuddin"],"suffixes":[]},{"propositions":[],"lastnames":["Zhang"],"firstnames":["Tianyou"],"suffixes":[]},{"propositions":[],"lastnames":["Xu"],"firstnames":["Zhao"],"suffixes":[]},{"propositions":[],"lastnames":["Lim"],"firstnames":["Pin"],"suffixes":[]},{"propositions":[],"lastnames":["Nadarajan"],"firstnames":["Sivakumar"],"suffixes":[]}],"month":"April","year":"2016","note":"Conference Name: IEEE Transactions on Industrial Electronics","keywords":"Aging, Data collection, Data models, Electrical Machine, Electrical machine, Feature extraction, Health Index, Hidden Markov models, Induction motors, Predictive models, Prognostics, Remaining Useful Life, health index (HI), prognostics, remaining useful life (RUL), sigkdd-rw","pages":"2633–2644","bibtex":"@article{yang_health_2016,\n\ttitle = {Health {Index}-{Based} {Prognostics} for {Remaining} {Useful} {Life} {Predictions} in {Electrical} {Machines}},\n\tvolume = {63},\n\tissn = {1557-9948},\n\tdoi = {10.1109/TIE.2016.2515054},\n\tabstract = {Many industries have a growing awareness in utilizing new technologies to improve the reliability and availability of their systems. 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