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 must**be maintained despite competitive pressures and the need for higher manufacturing efficiency and lower**production costs. As the Original Equipment Manufacturers (OEMs) move to leaner structures, the demands**placed on their equipment suppliers to assure quality and reliability is increasing. Typically the automotive**suppliers have fewer internal resources than OEMs with less capability and expertise to perform the required**quality and reliability analysis.****This proposal is a continuation of a project with D&V Electronics on developing Fault Detection and**Diagnosis (FDD) capabilities for end of production line testing of alternators. This cooperation has so far**resulted in the development of an algorithm referred to as the Industrial Extended Multi-Scale Principle**Components Analysis (IEMSPCA) that has previously been applied to automotive starters. Both vibration and**sound measurement were implemented in IEMSPCA. It performed FDD analysis in less than 15 seconds and**has 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 to**unknown 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 improving**diagnosis capabilities for a select range of fault conditions. The research outcomes will be implemented on a**D&V Electronics test cell platform.