Accelerated degradation testing (ADT) has been widely used for reliability prediction of highly reliable products. In many\napplications, ADT data consists of multiple degradation-related features, and these features are usually dependent. When dealing\nwith such ADT data, it is important to fully utilize the multiple degradation features and take into account their inherent\ndependency. This paper proposes a novel reliability-assessment method that combines Brownian motion and copulas to model\nADT data obtained from vibration signals. In particular, degradation feature extraction is first carried out using the raw vibration\nsignals, and a feature selection method quantifying feature properties, such as trend ability,monotonicity, and robustness, is adopted\nto determine the most suitable degradation features. Then, a multivariate s-dependent ADT model is developed, where a Brownian\nmotion is used to depict the degradation path of each degradation feature and a copula function is employed to describe the\ndependence among these degradation features. Finally, the proposed ADT model is demonstrated using the vibration-based ADT\ndata for an electric motor.
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