Incipient Fault Detection in Rotating Machines Using a Neural Network

这项工作的主要目的是开发一种利用人工神经网络检测旋转机械早期故障的新方法。由于中型感应电机的广泛应用和经济原因，本项目采用中型感应电机作为旋转机械的样机。这个概念可以很容易地从感应电机推广到其他旋转机械。根据感应电机的稳态特性，设计了一种用于检测感应电机早期故障的人工神经网络。然而，当电机遇到偶然的扰动时，该检测方案将被实时应用，在这种情况下，电机不会总是处于稳定状态。对故障检测器的不适当输入将在故障检测器处产生错误警报。将开发一种人工神经网络来滤除暂态测量值而保留稳态测量值，从而将正确的测量值反馈给故障检测器。这项工作的预期意义将是开发一种旋转机械在线早期故障检测器。该检测器由两部分组成：(1)测量干扰滤波人工神经网络；(2)早期故障检测人工神经网络。该检测器将能够检测到电机常见的早期故障，如匝间绝缘故障和轴承磨损，并将对电机中的干扰和测量噪声具有很强的鲁棒性。将制定理论以确保网络的性能，包括在不同电机运行条件下的培训(学习)和召回方案。将干扰和噪声过滤神经网络的性能与传统的噪声过滤方案进行比较

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