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: $ 7.63万
• 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: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=652777
• 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 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.

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，对设备供应商的要求**确保质量和可靠性正在提高。通常，汽车**供应商的内部资源比OEM的内部资源要少，具有执行所需的**质量和可靠性分析能力和专业知识的OEM。交流发电机生产线测试的诊断（FDD）功能。到目前为止，这种合作已经导致了一种算法的发展，该算法称为工业扩展的多尺度原理**组件分析（IEMSPCA），该原则以前已应用于汽车启动器。 IEMSPCA实施了振动和**声音测量。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，**已达到96％以上的检测成功率。 ，诊断，表征和适应**未知以及以前已知的断层条件在交流发电机的线结束测试期间发生。将使用声音，电压和当前测量值。将采用基于模型的策略来提高**诊断能力，以适应一系列故障条件。研究结果将在** D＆V电子测试单元平台上实施。