Online Condition Monitoring of Electric Machines

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Electric machines (EMs) (i.e., motors and generators) are commonly used in various domestic and industrial applications such as electric vehicles, machine tools, and wind turbines. Improving the performance and efficiency of EMs is of utmost significance to a wide array of industries. Although enormous effort has been undertaken over the decades to develop techniques and expert systems for automatic EM diagnosis, unfortunately, most of these systems cannot be used properly for industrial monitoring application due to poor reliability, with the occurrence of missed alarms (i.e., existing faults that are not identified) and false alarms (i.e., alarms triggered for reasons other than real faults). The objective of this research program is to develop new technologies and tools for intelligent diagnostics and prognostics (IDP) of EMs. The goal is to recognize the occurrence of an EM defect at its earliest stage, so as to prevent EM performance degradation, malfunction, or even catastrophic failure of the related facilities. When a potential problem arises, the IDP monitor can pinpoint the faulty components and estimate the remaining useful life of the faulty EM. In addition to improving production rates, the prognostic information can help to reduce operational costs because maintenance can be scheduled properly to avoid unexpected equipment shutdowns. Since the most commonly used EMs are induction motors (IMs) that also consume more than 50% of the electrical energy in the world each year, the proposed research will focus on IMs. The first research theme is to develop new signal processing techniques for more efficient denoising and IM fault detection. The second objective is to comprehensively assess the robustness of all available IM fault detection techniques corresponding to different operating and motor conditions. The third objective is to develop a new regulated predictor for multi-step-ahead forecasting of high-dimensional systems. Another objective of this innovative research program is to develop an IDP platform to integrate both diagnostic and prognostic information for a more positive assessment of IM conditions in real time. New strategies for system training will be proposed to improve the adaptive capability of the IDP to accommodate different IM conditions. This multidisciplinary research program will provide unique and leading-edge opportunities to train HQP in these related areas. In addition, the developed technologies and intelligent tools will benefit Canadian companies seeking to enhance their competitiveness in the global market by improving their production rates and quality and reducing costs.