As technology advances, more industrial devices are achieving higher reliability and longer lifespans. However, challenges such as limited sample sizes of experimental data and the complexity of factors influencing device degradation are becoming increasingly prevalent. Simultane
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As technology advances, more industrial devices are achieving higher reliability and longer lifespans. However, challenges such as limited sample sizes of experimental data and the complexity of factors influencing device degradation are becoming increasingly prevalent. Simultaneously, abundant degradation information from other data sources, including data from other components, historical batches, and different experimental stress levels, is available. Thus, there is an urgent need to find ways to fully utilize these multi-source data for industrial device reliability analysis. Therefore, this thesis proposes several data fusion methods to perform the reliability analysis of industrial devices that collect degradation data from different sources. The research addresses three primary research objectives: developing a data fusion-based framework for predicting the remaining useful life (RUL) of industrial devices that collect multivariate sensor data, formulating reliability analysis methods for degradation data from different batches of industrial devices, and establishing a framework for analyzing degradation data under varying experimental stresses and stress levels....
