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In this paper, a model based signal processing method is proposed to diagnose two faults under different load condition for permanent magnet synchronous machines. This method captures the spectrum of stator current in a compact representation. Then, its dimensionality is reduced using the discriminant analysis. Finally a support vector machine automatically classifies the health state of the system...
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...
Fault detection and isolation (FDI) is an important part of modern industrial systems, and plays a vital role in maintainability, safety, and reliability of process. We propose a novel FDI architecture based on a predictive model for fault-free process. We use the least-square support vector machine for identifying a nonlinear system and detecting its faults that may occur. Wavelet analysis on residual...
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