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Chao Hu, Byeng D. Youn, Pingfeng Wang, Joung Taek Yoon
Reliability Engineering & System Safety, Volume 103, July 2012
Abstract
Prognostics aims at determining whether a failure of an engineered system (e.g., a nuclear power plant) is impending and estimating the remaining useful life (RUL) before the failure occurs. The traditional data-driven prognostic approach is to construct multiple candidate algorithms using a training data set, evaluate their respective performance using a testing data set, and select the one with the best performance while discarding all the others. This approach has three shortcomings: (i) the selected standalone algorithm may not be robust; (ii) it wastes the resources for constructing the algorithms that are discarded; (iii) it requires the testing data in addition to the training data. To overcome these drawbacks, this paper proposes an ensemble data-driven prognostic approach which combines multiple member algorithms with a weighted-sum formulation. Three weighting schemes, namely the accuracy-based weighting, diversity-based weighting and optimization-based weighting, are proposed to determine the weights of member algorithms. The k-fold cross validation (CV) is employed to estimate the prediction error required by the weighting schemes. The results obtained from three case studies suggest that the ensemble approach with any weighting scheme gives more accurate RUL predictions compared to any sole algorithm when member algorithms producing diverse RUL predictions have comparable prediction accuracy and that the optimization-based weighting scheme gives the best overall performance among the three weighting schemes.
Additional Information:
By combining the predictions of all member algorithms, the ensemble approach achieves better accuracy in RUL predictions compared to any sole member algorithm. Furthermore, the ensemble approach has an inherent flexibility to incorporate any advanced prognostic algorithm that will be newly developed. To the best of our knowledge, this is the first study of an ensemble approach with three weighting scheme for the data-driven prognostics. Since the computationally expensive training process is done offline and the online prediction process requires a small amount of computational effort, the ensemble approach raises little concerns in the computational feasibility. Among the three weighting scheme, the optimization-based weighting scheme showed the capability of adaptively synthesizing the prediction accuracy and diversity of each member algorithm to enhance the accuracy of RUL predictions. Considering the enhanced accuracy and robustness in RUL predictions, the proposed ensemble approach leads to the possibility of effective condition-based maintenance practice and risk-informed lifetime management of high-risk engineered systems.
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