Incipient Fault Detection in Rotating Machines Using a Neural Network

使用神经网络检测旋转机器的初期故障

• 批准号: 8922727
• 负责人: Mo-Yuen Chow
• 金额: $ 11.99万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1990
• 资助国家: 美国
• 起止时间: 1990-07-01 至 1994-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8922727&HistoricalAwards=false
• 关键词: Incipient; Fault; Detection; Rotating; Machines

The main objective of the proposed work is to develop a new approach to detection of incipient faults in rotating machines by using artificial neural networks. Medium size induction motors are used as prototypes for rotating machines in this project due to their wide application and also for economic reasons. The concept can be easily generalized from induction motors to other rotating machines. The design of an artificial neural network to detect incipient faults of induction motors is based on the steady-state performance of the motor. However, the detection scheme will be applied in real time when the motor experience occasional disturbances, and in this case, the motor will not always be in steady-state. Inappropriate inputs to the fault detector will yield false alarms at the fault dector. An artificial neural network will be developed to filter out the transient measurements but retain the steady-state measurements and thus feed the correct measurement to the fault detector. The expected significance of the proposed work will be development of an on-line incipient fault detector for rotating machines. The detector is composed of two parts: (1) a measurement disturbance filter artificial neural network, and (2) an incipient fault detection artificial neural network. The detector will be able to detect the common incipient faults of the motor, such as turn-to-turn insulation failure and bearing wear, and will be robust to disturbances in the motor as well as to measurement noise. Theory will be developed to ensure the performance of the networks, including training (learning) and recalling schemes under different motor operating conditions. The performance of the disturbance and noise filtering neural network will be compared to the conventional noise filtering schemes

拟议工作的主要目的是通过使用人工神经网络开发一种新的方法来检测旋转机中的初期断层。 中等大小的感应电动机由于其广泛的应用以及出于经济原因而用作该项目中旋转机器的原型。 该概念可以很容易地从感应电动机到其他旋转机器。 人工神经网络检测感应电动机的初期故障的设计是基于电动机的稳态性能。 但是，当电动机体验偶尔出现干扰时，检测方案将实时应用，在这种情况下，电动机并不总是处于稳态中。 故障检测器的不适当输入将在故障驱动器上产生错误警报。 将开发人工神经网络，以滤除瞬态测量值，但保留了稳态测量，从而将正确的测量值馈送到断层检测器。 拟议工作的预期意义将是开发用于旋转机器的在线初始故障检测器。 检测器由两个部分组成：（1）测量干扰过滤器人工神经网络，以及（2）初始的故障检测人工神经网络。 检测器将能够检测电动机的常见发射断层，例如转弯的绝缘失败和轴承磨损，并且对电动机的干扰以及测量噪声非常有力。 将开发理论以确保网络的性能，包括培训（学习）和在不同的运动工作条件下召回方案。 将干扰和噪声过滤神经网络的性能与常规的噪声过滤方案进行比较