Fault detection and diagnosis (FDD) system for end of production line testing of alternators

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Product quality and reliability continues to be one of the top priorities for auto manufacturers, and these mustbe maintained despite competitive pressures and the need for higher manufacturing efficiency and lowerproduction costs. As the Original Equipment Manufacturers (OEMs) move to leaner structures, the demandsplaced on their equipment suppliers to assure quality and reliability is increasing. Typically the automotivesuppliers have fewer internal resources than OEMs with less capability and expertise to perform the requiredquality and reliability analysis.This proposal is a continuation of a project with D&V Electronics on developing Fault Detection andDiagnosis (FDD) capabilities for end of production line testing of alternators. This cooperation has so farresulted in the development of an algorithm referred to as the Industrial Extended Multi-Scale PrincipleComponents Analysis (IEMSPCA) that has previously been applied to automotive starters. Both vibration andsound measurement were implemented in IEMSPCA. It performed FDD analysis in less than 15 seconds andhas achieved more than 96% detection success rate when applied to known faults on 26377 starter test cases.In this project the IEMSPCA will be further developed to be able to detect, diagnose, characterize and adapt tounknown as well as previously known fault conditions occurring during end-of-line testing of alternators.Intelligent strategies such as deep learning will be applied and a larger array of signals including vibration,sound, voltage, and current measurements will be used. Model-based strategies will be applied for improvingdiagnosis capabilities for a select range of fault conditions. The research outcomes will be implemented on aD&V Electronics test cell platform.