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In this paper, a novel fault diagnosis technique is developed for permanent magnet synchronous motors (PMSM) using discrete wavelet transform and support vector machines based on stators current waveform. An adaptive filtering scheme is developed to remove the fundamental component of the current waveform in order to increase the accuracy of fault diagnosis. For this purpose, the instant rotational...
Monitoring acoustic emission from mechanical systems is an effective non-invasive way of diagnosing both system performance as well as short-term/long-term system failures. A difficulty however in fault detection in such systems is inter-class variability caused by non-uniform or unknown load conditions which decrease the classification accuracy. In this paper, a scattering transform is employed to...
Automatic fault diagnosis is an inseparable part of today's electromechanical systems. Advanced signal processing and machine learning techniques are required to address variabilities and uncertainties associated with the monitoring signals. In this paper, deep neural networks are employed to diagnose five classes of gearbox faults applied to three common monitoring signals, i.e. vibration, acoustic...
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