Intelligent diagnostics and prognostics for machinery systems

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Production quality, operational safety, and economics have a direct impact on the competitiveness in the global market of organizations in Canada. A reliable online monitoring system is crucially needed in industries to provide an early warning of potential damage that will subsequently prevent machinery performance degradation, malfunction, and catastrophic failure. The principal disadvantages of classical monitoring systems are the lack of reliability and robustness, especially for time-varying operating conditions. The goal of this research is to develop a new generation of intelligent diagnostic and prognostic (IDP) systems for online, more reliable monitoring of the health conditions of machinery. When such an IDP system is implemented, it will be able to recognize the health conditions of machinery. When a potential problem arises, the IDP system can pinpoint the faulty components, estimate the fault propagation trend, and forecast the remaining useful life of the damaged unit. The first focus of this research is to develop a new technique to extract specific rotation waveforms to detect bearing faults in rotary machinery. Many techniques have been proposed in the literature for bearing fault detection; however, each has its own merits and limitations. The secondary objective is to comprehensively investigate the robustness of all classical and proposed signal processing techniques for bearing and motor fault detection corresponding to different conditions. A new transformation technique has been developed to map a model-based paradigm to a neural fuzzy prototype in an effort to further improve the performance of the resulting scheme. An IDP intelligent tool is proposed to effectively integrate the processing information from both the classifier and the predictor to provide a more positive assessment of the health conditions of machinery. New criteria and strategies for online/offline training will be developed to extract new knowledge during real-time operations that will increase adaptive capabilities and robustness of the monitoring systems and accommodate different machinery conditions.