Incipient Fault Detection in Rotating Machines Using a Neural Network

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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