Model-based diagnosis and prognosis of induction motors under stator winding fault

Induction machines are widely used in industries and essential parts of industrial systems. They are found in various applications of marine propulsion, aerospace actuators, and traction in electric/hybrid vehicles. Despite their rugged construction, they are subject to fault due to aging, severe operating conditions, and harsh environments. Industrial surveys have shown that stator winding accounts for a significant portion of faults in electrical machines. Stator winding inter-turn short is one of the most common root cause of stator winding fault which can spread over and lead to catastrophic damages. Therefore, diagnosis and prognosis of this kind of fault can help increase the availability and reliability of electrical machines and the associated systems, and thereby, reduce maintenance cost, and support maintenance decision making. Diagnosis is the process of detecting fault occurrence, isolating the true fault from ambiguous factors, and estimating the severity information. Prognosis is subsequent to diagnosis process. It predicts the fault progression and estimates the remaining useful life, the time left before the machine must be stopped. Therefore, early fault detection is critical for an effective prognosis. However, the current research has the limitation on incipient fault detection due to the effects of ambiguous factors including the inherent asymmetry and voltage imbalance. Fault severity estimation/measurement is another essential part of diagnosis process. The fault is represented by two electrical fault parameters, viz. fault loop resistance and percentage of shorted turns, and the estimation of both these direct electrical fault parameters has achieved limited success. Therefore, the early fault detection and fault parameters estimation are required to be further investigated and improved. Regarding prognosis, it has been identified that prognosis using fault parameters for inter-turn fault is a new research to be explored. This has potential to provide an effective prognosis method based on direct physical parameters. This thesis iii aims to develop a model-based diagnosis and prognosis framework using the electrical fault parameters and propose techniques to address the aforementioned challenges. Model-based technique is the key approach to address the challenge of early fault detection under the influence of the ambiguous factors. Its strength lies in the ability to incorporate the electrical fault parameters in the model and hence the analysis of fault effects. Two generic methods are proposed. The first is a multiple-indicators approach, based on the multi-physics nature of electrical machines, in which certain supplementary indicators are concurrently used. The second is a decomposition method in which the effect of fault is decomposed from that of the ambiguous factors, so that a fault-dependent quantity, sensitive to only fault severity level, is extracted. The electrical fault parameters are direct severity quantities, but they are unknown and unmeasurable. In this thesis, an estimation process is proposed to identify these parameters, linking the diagnosis and prognosis process. The filter-based approach, using particle filter, is applied to estimate the fault parameters. Particle filter is chosen since it can be applied to nonlinear systems under non-Gaussian noises. Also, a significant advantage of particle filter-based approach is that the statistical quantities of the particles can be employed to quantify the performance of remaining useful life estimation results based on the estimates of fault parameters. Other methods for estimation of electrical fault parameters are also proposed. They are based on unique characteristics of electrical machines. The first method is based on the characteristic of electrical machines under sinusoidal excitation voltages, in which differential equations can be transformed to trigonometry equations, and thereby, a closedform solution for the estimates of fault parameters can be obtained. The second method is based on the multiple forms of model of electrical machines in which sequence component model is exploited to derive an equality constraint, and the constraint is employed to simplify state-space model, which is used to estimate the fault parameters. The theoretical development is supported by experimental results, where various fault severity levels and different loads and voltage imbalance conditions are taken into account to validate the incipient fault detection, electrical fault parameter estimation, degradation model identification and remaining useful life prediction. The proposed framework and associated techniques are generic in nature and can be diversified to include other faults/ machines. iv