Author:
Jiang Renyan,Xiong Binbin
Abstract
Abstract
Degradation processes are often multidimensional. Modeling such degradation processes needs to address two key issues: indicator fusion and degradation model selection; and they have been separately addressed in the literature. This paper proposes a hybrid approach to jointly address these two issues. The proposed approach first fuses multiple degradation indicators into a composite degradation indicator. This fusion step involves data normalization, aggregation model selection and determination of indicator weights. After the fusion step, the problem becomes one-dimensional, and the existing method to select the degradation model for a one-dimensional degradation process can be applied. The resulting model obtained from the proposed approach can be a two-phase model; and the model for the second phase has a closed-form expression. This considerably facilitates residual life prediction. A real-world example is included to illustrate the proposed approach and its appropriateness.
Reference16 articles.
1. A Data-Level Fusion Model for Developing Composite Health Indices for Degradation Modeling and Prognostic Analysis;Liu;IEEE Trans. Autom. Sci. Eng.,2013
2. A data-level fusion approach for degradation modeling and prognostic analysis under multiple failure modes;Chehade;Journal of Quality Technology,2018
3. Statistical degradation modeling and prognostics of multiple sensor signals via data fusion: A composite health index approach;Song;IIE Trans.,2018
4. A standard-based approach for multi-criteria performance evaluation of engineered systems;Jiang;Reliab. Eng. Syst. Saf.,2018