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，尽管面临竞争压力，需要更高的制造效率和更低的生产成本，但必须**保持这些。随着原始设备制造商(OEM)转向更精简的结构，对其设备供应商确保质量和可靠性的要求**正在增加。通常，汽车**供应商的内部资源比OEM少，执行所需的**质量和可靠性分析的能力和专业知识较少。*本提案是D&V Electronics与D&V Electronics关于开发交流发电机生产线末端测试的故障检测和**诊断(FDD)能力的项目的延续。到目前为止，这种合作**导致了一种称为工业扩展多尺度原理**成分分析(IEMSPCA)的算法的开发，该算法以前已应用于汽车起动器。在IEMSPCA中实现了振动和**声测量。它在不到15秒的时间内进行故障诊断分析，**在26377个起动机测试案例上应用于已知故障时，检测成功率达到96%以上。*在该项目中，IEMSPCA将进一步发展，能够检测、诊断、表征和适应交流发电机线端测试过程中发生的**未知和先前已知的故障情况。**将应用深度学习等智能策略，并将使用更大的信号阵列，包括振动、**声音、电压和电流测量。将应用基于模型的策略来提高**针对选定范围的故障条件的诊断能力。研究成果将在**D&V电子测试单元平台上实现。