A Universal Tool for Fault Detection in Line Start Permanent Magnet Synchronous Motors

In several industrial applications, especially in oil and gas plants, induction motors are being gradually replaced by line start permanent magnet synchronous motors (LSPMSMs). LSPMSMs are the highest efficiency motors in the market, it is efficiency exceed the IE4 (International Efficiency 4) efficiency Level. Therefore, such motors can be considered as a good alternative for users who consider energy saving a priority. As the number of LSPMSMs used in different fields is increasing, the maintenance scheme for these motors becomes important. In practical applications, LSPMSM can experience different types of failures such as broken bars, eccentricity, as well as stator windings faults. Such failures threaten the normal manufacturing, interrupt the normal operation, and result in a significant loss of revenue. An efficient fault detection technique can reduce the maintenance expenses by preventing the high cost failures and unplanned downtimes. Despite the importance of fault detection in LSPMSMs, the current literature shows that there is no scientific manuscript on the diagnosis of LSPMSMs under stator inter-turn fault and/or inter-turn and broken bar and/or inter-turn and eccentricity or combination of the three faults. Accordingly, the aim of this project is to develop a universal diagnosing tool for detecting faults (stator inter-turn fault, broken bar, and eccentricity) in LSPMSMs. In this study, a comprehensive theoretical investigation on LSPMSM possible faults will be conducted. A finite element-based model for LSPMSMs under several abnormalities (inter-turn fault, broken bars and eccentricity) will be developed and implemented. The finite element model will be tested under different faults type and severity at different loading conditions. Then, simulation results will be used to extract representative faults features, which will be used in the development of a diagnosing tool for detecting the type and severity of LSPMSM abnormalities. Finally, the accuracy and robustness of the proposed diagnostic tool will be tested under unseen faults cases.