Fault diagnosis and failure prognosis of electrical machines

Early detection, categorization arid monitoring of faults can ensure safe and reliable operation, and increase the lifetime of a system. Fault is a condition corresponding to initial damage to a component or subsystem that, although does not affect the performance of it, can escalate to a failure. Diagnosis is the early detection of faults in the system and the assessment of its severity. On the other hand, failure prognosis is to identify the evolution of the fault condition and to predict the remaining useful life of the system.;The goal of this work is to develop a framework for fault diagnosis arid failure prognosis which can detect and categorize the condition of an electromechanical system, and predicts its remaining useful life. In this work, methods arc presented to identify transient faults using time-frequency analysis. The fault features are extracted from the motor current using Short Time Fourier Transform, Undecimated Wavelet Transform, Wigner Transform and Choi-Williams Transform. The presence of a fault is detected using spectrum energy density analysis and the categorization is performed by the pattern recognition classifiers, linear discriminant classifier and the nearest neighborhood classifier. The efficiency of each transform, to represent the underlying transient phenomenon, is compared by using Fisher discriminant ratio.;A prognosis algorithm is developed which predicts the remaining useful life of the system. Both the diagnosis and prognosis algorithms use the same time-frequency features extracted from the motor current. The prognosis algorithm is developed based on the statistical Hidden Markov Model. The model has three elements, state transition probabilities, state dependent observation densities and initial state probability distributions. Large data sets are required for the training of these elements, which are generally not available in the case of electromechanical systems. Methods are presented for the training of these elements from sparse data sets. For the computation of state transition probabilities, a method based on the Matching Pursuit decomposition is presented. The state dependent observation probability densities are defined as parametric densities and their statistics are computed from the experimental observations.;A survey of the state of the art diagnosis arid prognosis methods is also presented in the dissertation. Possible faults in electromechanical system and their manifestation in the system parameters, and the experimental setup are also included. The proposed method is illustrated by examples using data collected from the experimental setup.