Industrial Extended Multi-Scale Principle Components Analysis for Fault Detection and Diagnosis of Car Alternators and Starters

Abstract

Quality assurance of electrical components of cars such as alternators and starters is an important consideration due to both commercial and safety reasons. The focus of this research is to develop a complete Fault Detection and Diagnosis (FDD) solution for alternators and starters for their implementation in test cells. The FDD would enable more reliable testing of production line parts without compromising high production throughput. Our proposed solution includes three elements: (1) background noise elimination; (2) fault detection and analysis; and (3) fault classi cation for fault type identi cation. Noise gating, Extended Multi-Scale Principle Component Analysis (EMSPCA), and Logistic Discriminant classi er were used to perform these three elements. The FDD strategy detects and extracts fault signatures from multiple sensors (which are vibration and sound measurements in this research). Included in this strategy is ltering of the background noise in sound measurements to enable operation and maintain FDD performance in noisy conditions. The EMSPCA is the core of the FDD strategy. EMSPCA breaks the fault into time-frequency scales using wavelets and applies Principle Component Analysis (PCA) on each scale. This reveals the signature of the fault. The fault signature is then examined by a classi er to match it with the correct type of faults. The total FDD strategy is automated and no operator intervention is required. The advantages of the proposed FDD strategy are: (1) high accuracy in detection and diagnosis; (2) robustness in noisy industrial conditions; and (3) no need for operators' intervention. These advantages make the proposed FDD strategy a promising candidate for mass industrial applications.