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

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 尽管需要更轻的部件和更低的制造成本，但仍保持不变。作为原始设备 制造商(OEM)转向更精简的结构，要求设备供应商确保 他们生产的产品的质量和可靠性都在提高。通常情况下，汽车供应商拥有的 内部资源低于原始设备制造商，其能力和专业知识较少，无法实现所需的质量和 可靠性分析。 在这个项目中，我们建议对发电机实施故障检测和诊断(FDD)策略 还有开胃菜。目前的行业标准采用在装配线末端进行电气和机械测试。 这些当前的方法不能检测所有故障，因此需要改进的FDD策略。在……里面 这个项目我们建议使用声学和振动测量，并将这些引入到生产线的末尾 生产测试系统。这种类型的数据的使用并不新鲜，但它们在生产中的FDD实现 事实证明，具有高背景声音和振动的测试环境具有挑战性。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， 小波和一种新的滤波策略，称为平滑变结构滤波器(SVSF)。通过比较 对照基线，这些算法将用于交流发电机和 开胃菜。

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.