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

用于交流发电机生产线末端测试的故障检测和诊断 (FDD) 系统

• 批准号: 486107-2015
• 负责人: Habibi, Saeid
• 金额: $ 9.66万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=618548
• 关键词: Fault; detection; diagnosis; FDD; system

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.

产品质量和可靠性仍然是汽车制造商的首要任务之一，尽管有竞争性压力，并且需要提高制造效率和降低生产成本，但必须保持这些优先事项。随着原始设备制造商（OEM）转移到更精简的结构时，其设备供应商的要求提高了质量和可靠性。通常，汽车供应商的内部资源比OEM的内部资源少于OEM，具有执行所需质量和可靠性分析的能力和专业知识。该提案是一个项目的延续，该项目的D＆V电子设备用于开发故障检测和登录功能（FDD），用于生产线测试交流发电机发电机发电机发电机发电机发电机发电机的最终生产线测试功能。这种合作在开发的算法中是如此挑剔，该算法称为工业扩展的多尺度原则分析（IEMSPCA），该算法以前已应用于汽车启动器。 IEMSPCA实现了振动和节目测量。它在不到15秒的时间内进行了FDD分析，并且在26377启动器测试用例上应用已知故障时，它实现了96％以上的检测成功率。在该项目中，IEMSPCA将进一步开发，以便能够检测，诊断，表征和适应Tounkoney以及以前已知的断层条件在交流发电机的线结束测试中发生。将应用诸如深度学习的智能策略，并将使用较大的信号，包括振动，声音，电压和当前测量值。基于模型的策略将用于改善特定故障条件范围的诊断能力。研究结果将在AD＆V电子测试单元平台上实施。