US-Egypt Cooperative Research: Online Fault Diagnostics for Induction Motor Drive Systems Through Electronic Signals and Artificial Intelligence Techniques

美埃合作研究：通过电子信号和人工智能技术对感应电机驱动系统进行在线故障诊断

• 批准号: 0609731
• 负责人: Nabeel Demerdash
• 金额: $ 2.65万
• 依托单位: Marquette University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-01-01 至 2008-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0609731&HistoricalAwards=false
• 关键词: US; Egypt; Cooperative; Research; Online

0609731DemerdashDescription: This award is to support a cooperative research by Dr. Nabeel Demerdash, Department of Electrical and Computer Engineering, Marquette University, Milwaukee, Wisconsin and Dr. Faeka Khater, Electronic Research Institute, Cairo, Egypt. They plan to investigate on-line fault diagnostics for induction motor drives-system through electronic signals and artificial intelligence techniques. The goal is development of online fault diagnostic techniques using electronic signals such as motor terminal voltages and currents in conjunction with innovative signal processing diagnostic techniques and modern artificial intelligence methods such as Neural Networks and Gaussian Mixture Models. The drive system to be studied is a sensor-less Field Oriented Controlled (F.O.C) one without the need for a speed sensing device. The proposed technique will diagnose failure occurrences and identify the severity of the fault. Modeling and analytical algorithms will be used to predict the performance and enable the implementation of the diagnostic system in the drive controller. Experimental work on a 5-hp induction machine and analysis of recorded results will be used to evaluate and validate the developed fault diagnostic techniques.Intellectual Merit: The problem of fault diagnostics in modern motor-drive systems (adjustable/variable speed drives) is a challenging and technically difficult one, especially if the motor-drive system is of the field oriented (vector control) closed-loop class. In such a class of motor-drives, whenever a fault or defect materializes in the motor, a corresponding compensating action takes place within the drive control system. This action usually tends to mask the existence of a fault particularly in its early stages. This research will address this compensating fault masking problem, and development of new diagnostic methods and means to detect such faults in the presence of drive control compensation actions. The results would constitute a major original contribution to the state of the art in motor-drive fault detection and diagnostics.Broader Impact: Modern AC motor-drive systems are extensively used throughout the manufacturing, processing, and service industries, including critical services such as auxiliaries and life support systems in the medical field. The sophistication of the controls employed in such drives complicates the task of diagnosing faults in such systems before faults evolve into catastrophic or serious equipment failure. The unmasking of such faults in the presence of closed-loop controls would constitute a significant improvement in avoiding dangerous failures and associated consequences in practical applications, which increasingly rely on AC motor-drive systems. The results of the proposed work could have a great impact on the design of future electric drive systems to have an online fault diagnosis and detection system. The project involves high-level research, teaching, and application, as well as considerable breadth in culture exchange through the joint effort by researchers and students from the US and Egypt. Two Marquette University graduate students will participate in the project.This project is being supported under the US-Egypt Joint Fund Program, which provides grants to scientists and engineers in both countries to carry out these cooperative activities.

0609731 Demerdash描述：该奖项是为了支持威斯康星州密尔沃基的马奎特大学电气和计算机工程系的Nabeel Demerdash博士和埃及开罗电子研究所的Faeka Khater博士的合作研究。 他们计划通过电子信号和人工智能技术研究感应电动机驱动系统的在线故障诊断。目标是开发在线故障诊断技术，使用电子信号，如电机端电压和电流，结合创新的信号处理诊断技术和现代人工智能方法，如神经网络和高斯混合模型。所研究的驱动系统是一种无速度传感器的磁场定向控制系统。所提出的技术将诊断故障发生，并确定故障的严重性。建模和分析算法将用于预测性能，并实现驱动控制器中的诊断系统。 5马力感应电机的实验工作和记录结果的分析将被用来评估和验证开发的故障诊断technology.Intellectual优点：故障诊断在现代电机驱动系统（可调/变速驱动器）的问题是一个具有挑战性的和技术上困难的，特别是如果电机驱动系统是磁场导向（矢量控制）闭环类。在这种类型的电动机驱动器中，每当在电动机中出现故障或缺陷时，在驱动控制系统内发生相应的补偿动作。这种作用通常会掩盖故障的存在，特别是在故障的早期阶段。本研究将解决这一补偿故障掩蔽问题，并开发新的诊断方法和手段，以检测这种故障的驱动控制补偿行动的存在。结果将构成一个主要的原始贡献，最先进的电机驱动故障检测和诊断。更广泛的影响：现代交流电机驱动系统广泛应用于整个制造业，加工业和服务业，包括关键服务，如辅助设备和生命支持系统在医疗领域。在这种驱动器中采用的控制的复杂性使得在故障发展成灾难性或严重的设备故障之前诊断这种系统中的故障的任务复杂化。 在存在闭环控制的情况下，这种故障的揭露将构成在实际应用中避免危险故障和相关后果方面的重大改进，实际应用越来越依赖于AC电机驱动系统。 所提出的工作的结果可能会有很大的影响，未来的电力驱动系统的设计，有一个在线故障诊断和检测系统。 该项目涉及高水平的研究，教学和应用，以及通过来自美国和埃及的研究人员和学生的共同努力进行的相当广泛的文化交流。两名马奎特大学的研究生将参加该项目。该项目得到了美国-埃及联合基金计划的支持，该计划为两国的科学家和工程师提供赠款，以开展这些合作活动。