Advanced fault detection and diagnosis system

产品质量和可靠性仍然是制造商的首要任务之一，尽管需要更轻的部件和更低的制造成本，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，对设备供应商的要求也越来越高，以确保其生产的产品的质量和可靠性。通常情况下，供应商的内部资源比原始设备制造商少，执行所需质量和可靠性分析的能力和专业知识也更少。在McMaster，我们已经开发并实施了一系列应用的故障检测和诊断（FDD）策略，包括电动机和内燃机测试。在所考虑的应用程序中，我们的FDD算法已经达到了100%的故障检测率和96%的诊断率。在这些应用中，声学和振动测量以组合形式与主成分分析、小波分析和数字滤波结合使用。这是一个相当大的改进相比，目前的行业标准，这些采用电气和机械测试的阈值，是不能检测到所有的故障，并有有限的故障诊断能力。我们的先进FDD系统由两部分组成：1）FDD算法和软件;和2）一个新的无线传感器和计算（WISC）平台，已被设计在一个通用的形式为旋转机械。我们的FDD算法的未来应用可以包括所有的系统表现出周期性的行为。这可能包括心脏等生物系统。对我们的FDD系统进行了市场评估，并确定了我们的技术在汽车系统和风力涡轮机上应用的大量机会。该建议涉及我们的先进FDD系统的定制，以应用于这两个特定领域。这种定制原型是该技术商业化的关键一步，将成为初创企业的基础。

Product quality and reliability continues to be one of the top priorities for manufacturers, 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 suppliers have fewer internal resources than the OEMs with less capability and expertise to perform the required quality and reliability analysis. At McMaster, we have developed and implemented a Fault Detection and Diagnostic (FDD) strategy for a range of applications including electric motor and internal combustion engine testing. In the applications considered, our FDD algorithm has achieved a fault detection rate of 100% and a diagnosis rate of 96%. In these applications, acoustic and vibration measurements were used in a combined form in conjunction with Principal Components Analysis, Wavelets and digital filtering. This is a considerable improvement compared to current industry standards; these employ electrical and mechanical tests against thresholds that are not able to detect all faults and have limited capability in fault diagnosis.Our Advanced FDD system consists of two parts: 1) the FDD algorithm and software; and 2) a novel WIreless Sensory and Computing (WISC) platform that has been designed in a generic form for rotating machinery. The future applications of our FDD algorithms can include all systems exhibiting a periodic behavior. This may include biological systems such as the heart. A market assessment of our FDD system was performed and identified considerable opportunities for our technology for its application to automotive systems as well as wind turbines. This proposal pertains to the customisation of our Advanced FDD system for its application in these two specific sectors. This custom prototype is a critical step in the commercialization of this technology and will form the basis for a start-up venture.