Online Condition Monitoring of Electric Machines

电机在线状态监测

• 批准号: RGPIN-2016-06311
• 负责人: Wang, Wilson
• 金额: $ 2.77万
• 依托单位: Lakehead University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=687179
• 关键词: Online; Condition; Monitoring; Electric; Machines

Electric machines (EMs) (i.e., motors and generators) are commonly used in various domestic and industrial applications such as electric vehicles, machine tools, and wind turbines. Improving the performance and efficiency of EMs is of utmost significance to a wide array of industries. Although enormous effort has been undertaken over the decades to develop techniques and expert systems for automatic EM diagnosis, unfortunately, most of these systems cannot be used properly for industrial monitoring application due to poor reliability, with the occurrence of missed alarms (i.e., existing faults that are not identified) and false alarms (i.e., alarms triggered for reasons other than real faults). The objective of this research program is to develop new technologies and tools for intelligent diagnostics and prognostics (IDP) of EMs. The goal is to recognize the occurrence of an EM defect at its earliest stage, so as to prevent EM performance degradation, malfunction, or even catastrophic failure of the related facilities. When a potential problem arises, the IDP monitor can pinpoint the faulty components and estimate the remaining useful life of the faulty EM. In addition to improving production rates, the prognostic information can help to reduce operational costs because maintenance can be scheduled properly to avoid unexpected equipment shutdowns. Since the most commonly used EMs are induction motors (IMs) that also consume more than 50% of the electrical energy in the world each year, the proposed research will focus on IMs. The first research theme is to develop new signal processing techniques for more efficient denoising and IM fault detection. The second objective is to comprehensively assess the robustness of all available IM fault detection techniques corresponding to different operating and motor conditions. The third objective is to develop a new regulated predictor for multi-step-ahead forecasting of high-dimensional systems. Another objective of this innovative research program is to develop an IDP platform to integrate both diagnostic and prognostic information for a more positive assessment of IM conditions in real time. New strategies for system training will be proposed to improve the adaptive capability of the IDP to accommodate different IM conditions. This multidisciplinary research program will provide unique and leading-edge opportunities to train HQP in these related areas. In addition, the developed technologies and intelligent tools will benefit Canadian companies seeking to enhance their competitiveness in the global market by improving their production rates and quality and reducing costs.

电机（即电动机和发电机）通常用于各种家用和工业应用，如电动汽车，机床和风力涡轮机。提高新兴市场的性能和效率对许多行业都具有极其重要的意义。尽管在过去的几十年里，人们已经付出了巨大的努力来开发用于自动电磁诊断的技术和专家系统，但不幸的是，由于可靠性差，大多数这些系统不能正确地用于工业监测应用，并且会发生漏报（即未识别的现有故障）和误报（即由于实际故障以外的原因触发的警报）。该研究计划的目标是开发em智能诊断和预后（IDP）的新技术和工具。目标是在早期阶段识别EM缺陷的发生，从而防止EM性能下降，故障，甚至相关设施的灾难性故障。当潜在问题出现时，IDP监控器可以精确定位故障组件并估计故障EM的剩余使用寿命。除了提高生产率外，预测信息还可以帮助降低运营成本，因为可以正确安排维护以避免意外设备停机。由于最常用的电机是感应电机（IMs），其每年消耗的电能也超过全球的50%，因此拟议的研究将集中在IMs上。第一个研究主题是开发新的信号处理技术，以更有效地去噪和IM故障检测。第二个目标是综合评估所有可用的IM故障检测技术对应于不同的操作和电机条件的鲁棒性。第三个目标是开发一种新的调节预测器，用于高维系统的多步超前预测。这项创新研究计划的另一个目标是开发一个IDP平台，将诊断和预后信息整合起来，以便对IM状况进行更积极的实时评估。将提出新的系统训练策略，以提高国内流离失所者适应不同IM条件的能力。这个多学科的研究项目将为HQP在这些相关领域的培训提供独特和领先的机会。此外，开发的技术和智能工具将有利于加拿大公司通过提高生产率和质量以及降低成本来提高其在全球市场上的竞争力。