Accelerated degradation testing (ADT) has been widely used for reliability prediction of highly reliable products. In many applications, ADT data consists of multiple degradation-related features, and these features are usually dependent. When dealing with such ADT data, it is important to fully utilize the multiple degradation features and take into account their inherent dependency. This paper proposes a novel reliability-assessment method that combines Brownian motion and copulas to model ADT data obtained from vibration signals. In particular, degradation feature extraction is first carried out using the raw vibration signals, and a feature selection method quantifying feature properties, such as trendability, monotonicity, and robustness, is adopted to determine the most suitable degradation features. Then, a multivariate s-dependent ADT model is developed, where a Brownian motion is used to depict the degradation path of each degradation feature and a copula function is employed to describe the dependence among these degradation features. Finally, the proposed ADT model is demonstrated using the vibration-based ADT data for an electric motor.
ASJC Scopus subject areas
- Civil and Structural Engineering
- Condensed Matter Physics
- Geotechnical Engineering and Engineering Geology
- Mechanics of Materials
- Mechanical Engineering