Missing data interpolation and multi‐sensors integration and its application in accelerated degradation data

Author:

Wang Changxi123ORCID,Wu Tong1,Wang Ting4,Li Kang123

Affiliation:

1. West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu Sichuan China

2. Med‐X Center for Informatics Sichuan University Chengdu China

3. Sichuan University ‐ Pittsburgh Institute Sichuan University Chengdu China

4. Center of Biostatistics Design, Measurement and Evaluation (CBDME) West China Hospital Sichuan University Chengdu Sichuan China

Abstract

AbstractMultiple sensors are commonly used for accelerated degradation monitoring. Since different sensors may be sensitive at different stages of the accelerated degradation process and each sensor dataset may contain only partial information of the unit degradation, then integration approaches of the accelerated degradation data from multiple sensors can effectively improve degradation modeling and life prediction accuracy. We present a non‐parametric approach that assigns weights to each sensor based on the dynamic clustering of the sensors' observations. Missing data are common in degradation data acquisition, especially when multiple sensors are used. We provide two approaches for data interpolation: the nonlinear Brownian bridge and the inverse Gaussian bridge when the underlying degradation paths follow the Brownian motion process and inverse Gaussian process, respectively. The stochastic bridges capture the nonlinearity and uncertainties of the degradation processes. The data integration model and stochastic bridge models are validated with real accelerated fatigue crack growth data monitored with multiple NDT sensors. The proposed models provide an accurate accelerated degradation path and reliability prediction.

Funder

National Natural Science Foundation of China

National Basic Research Program of China

Publisher

Wiley

Subject

Management Science and Operations Research,Safety, Risk, Reliability and Quality

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