Abstract

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.

Highlights

  • Nowadays, products are made more reliable due to the advances of design and manufacturing and to the improvement of materials technology

  • Afterwards, a copula function is used to describe the dependence of different degradation features resulting in a multivariate s-dependent accelerated degradation testing (ADT) model

  • Crest factor, and Hilbert-Huang Transform (HHT) marginal energy spectrum entropy (HMESE) fitness were all greater than 0.7, they were selected as the degradation indicators for the motors

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Summary

Introduction

Products are made more reliable due to the advances of design and manufacturing and to the improvement of materials technology. In order to find the degradation trend of a product and predict its lifetime using ADT, the product’s performance indicators must be defined first Such indicators may include the product’s functional or performance parameters, and those special features extracted from raw sensor data [1], such as vibration, force and acoustic signals, temperature, and voltage. Based on the related literature, one can see that some technical problems have not been resolved in the study of vibration-based ADT with multiple features, including fitness analysis of degradation indicators, development of multivariate s-dependent ADT model, and parameter estimation. To overcome these challenges, a novel reliability modeling method for vibration-based CSADT with multiple dependent features is proposed in this paper.

Framework of the Proposed Model
The Degradation Indicator Fitness Analysis
Modeling of CSADT with Multiple Features
Use of Copulas for Multiple Features
Case Study
Conclusions
Full Text
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