Diagnosis of Normal and Abnormal Operations of Induction Motors in ASDS by a Coupled Finite Element-Network Technique

Non-invasive diagnosis of abnormal/faulty rotor conditions of broken bars, broken end-ring connectors as well as static and dynamic airgap eccentricities in squirrel-cage induction motors in Adjustable Speed Drives (ASDs) is a well-known problem that still warrants further investigations. In this regard, there are several important issues that need to be addressed with respect to improvement of the reliability of a drive\u27s condition monitoring and diagnostics. One of these issues is that at present an historical record of performance of a motor or drive is required to detect an increase in the severity of these abnormal conditions. However, it is often the case in industry that such data is not readily obtainable prior to the on-set of the problem in the field. In addition, the success of some fault detection equipment developed over the past several years is limited. This is because detection depends on their accuracy of measurement as well as their ability to successfully discriminate between the subtleties that are hidden within the normal and abnormal motor/drive performance signatures. Analyses and diagnoses of these various abnormal conditions by use of present detection techniques as reported in the literature have been restricted, by and large, to the identification of the fundamental component and its associated side bands in the FFT frequency spectra of the motor time-domain line current waveforms. Relying on the identification of these side band frequency components to detect these various abnormalities leaves something to be desired with regard to specific diagnosis of, and differentiation between, broken bars/connectors and airgap eccentricities. In recognition of these facts, the possibility of future application of numerical model-based predictive techniques, which have the potential for enhanced accuracy and consistency, is increasingly becoming attractive and desirable. Nonetheless, present model-based predictive techniques have been directed almost exclusively to the identification of effects of these various abnormalities on the frequency contents of motor line current signatures. Moreover, very little attention, if any, has been given to the importance of second-order phenomena that inherently occur in these machines as a result of these abnormalities and their consequent effects on motor parameters and performance characteristics. Thus, there is a lack of rigorous and comprehensive predictive simulation models and results that can confirm and correlate with field experiences and data. Accordingly, this dissertation presents a new numerical model that addresses the majority of the issues elucidated above, for rigorous analysis and more comprehensive non-invasive model-based predictive diagnosis of the above-mentioned rotor abnormalities. The proposed technique has several attractive and potential advantages over most of the models previously reported in the archival literature on improving the reliability and consistency motor/drive diagnostics of present practical diagnostic schemes