Development of a detection and diagnosis systems for alternator and starter testing

开发用于交流发电机和起动机测试的检测和诊断系统

• 批准号: 451857-2013
• 负责人: Habibi, Saeid
• 金额: $ 7.03万
• 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: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=575443
• 关键词: Development; detection; diagnosis; systems; alternator

Product quality and reliability continues to be one of the top priorities for auto manufactures, and these must be maintained in spite of the need for lighter parts and lower manufacturing costs. As the Original Equipment Manufacturers (OEMs) move to leaner structures, the demands placed on the equipment suppliers to assure quality and reliability of the products they produce is increasing. Typically the automotive suppliers have fewer internal resources than the OEMs with less capability and expertise to perform the required quality and reliability analysis. In this project we propose to implement a fault detection and diagnostic (FDD) strategy for electric alternators and starters. Current industry standards employ electrical and mechanical tests at the end of the assembly line. These current methods are not able to detect all faults, and thus there is a need for improved FDD strategies. In this project we propose to use acoustic and vibration measurements and introduce these to the end of line production test systems. The use of this type of data is not new, but their FDD implementation in production test environments with high background sounds and vibrations has proved challenging. McMaster has developed a number of unique algorithms that provide a capability to detect faults. These methods have proven effective in isolating the signals generated by faulty part from the background noise. In this project, acoustic and vibration measurements are used in a combined form in conjunction with Principal Components Analysis, Wavelets and a new filtering strategy referred to as the Smooth Variable Structure Filter (SVSF). By comparing against baseline, these algorithms will be used for the isolation and detection of faults in alternators and starters.

产品质量和可靠性仍然是汽车制造商的首要任务之一，这些必须是 尽管需要更轻的部件和更低的制造成本，与原设备 制造商（OEM）转向更精简的结构，对设备供应商的要求是确保 他们生产的产品的质量和可靠性正在提高。一般来说，汽车供应商拥有的 内部资源比原始设备制造商更少的能力和专业知识来执行所需的质量和 可靠性分析 在这个项目中，我们提出了实现一个故障检测和诊断（FDD）的策略，为电动发电机 和开胃菜目前的行业标准在装配线的末端进行电气和机械测试。 这些当前方法不能检测所有故障，因此需要改进的FDD策略。在 在这个项目中，我们建议使用声学和振动测量，并将其引入到生产线的末端 生产测试系统。使用这种类型的数据并不新鲜，但它们在生产中的FDD实现 具有高背景声音和振动的测试环境已被证明是具有挑战性的。麦克马斯特有 开发了许多独特的算法，提供了检测故障的能力。这些方法已经证明 有效地将故障部件产生的信号与背景噪声隔离。在这个项目中，声学 并且振动测量以结合主成分分析的组合形式使用， 小波变换和一种新的滤波策略称为平滑变结构滤波器（SVSF）。通过比较 与基线相比，这些算法将用于隔离和检测交流发电机中的故障， 开胃菜。