Abstract
Industrial Internet of Things has significantly boosted predictive maintenance for complex industrial systems, where the accurate prediction of remaining useful life (RUL) with high-level confidence is challenging. By aggregating multiple informative sources of system degradation, information fusion can be applied to improve the prediction accuracy and reduce the uncertainty. It can be performed on the data-level, feature-level, and decision-level. To fully exploit the available degradation information, this article proposes a hybrid fusion method on both the data level and decision level to predict the RUL. On the data level, genetic programming (GP) is adopted to integrate physical sensor sources into a composite health indicator (HI), resulting in an explicit nonlinear data-level fusion model. Subsequently, the predictions of the RUL based on each physical sensor and the developed composite HI are synthesized in the framework of belief functions theory, as the decision-level fusion method. Moreover, the decision-level method is flexible for incorporating other statistical data-driven methods with explicit estimations of the RUL. The proposed method is verified via a case study on NASA's C-MAPSS data set. Compared to the single-level fusion methods, the results confirm the superiority of the proposed method for higher accuracy and certainty of predicting the RUL.
Original language | English |
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Article number | 9343303 |
Journal | IEEE Internet of Things Journal |
Volume | 8 |
Issue number | 11 |
Pages (from-to) | 9045-9058 |
Number of pages | 14 |
ISSN | 2327-4662 |
DOIs | |
Publication status | Published - 1 Jun 2021 |
Bibliographical note
Publisher Copyright:© 2014 IEEE.
Keywords
- Industrial Internet of Things (IIoT)
- information fusion
- multiple sources
- prognostics
- remaining useful life (RUL)