A novel intelligent system for machinery condition monitoring and failure prognostics

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The development of reliable condition monitoring and failure prognostic systems has been the focus of various undertakings in a wide array of industries to prevent machine/system performance degradation, malfunction, or even catastrophic failure. Although a variety of methods have been proposed in the literature, reliable assessment of machine current and future health conditions still remains a challenging task in both research and real-world industrial applications. This is because most machines are complex in structure and are operating in a noisy and time-varying environment, wherein the traditional signal processing techniques, pattern classification schemes, and failure prognostic approaches cannot be effectively implemented. Correspondingly, the objective of my proposed research program is to develop a novel intelligent system to address these challenges. The ultimate goal is to develop more reliable techniques for industrial monitoring and prognostic applications so as to improve product quality and to reduce the manufacturing cost in Canadian companies.