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To reduce data-storage costs and enhance high accuracy of industrial process fault detection, a data driven fault diagnosis method is proposed based on diffusion maps and hidden Markov model. Firstly, the correlation dimension of sample data is calculated. Secondly, the high-dimensional eigenvectors are extracted into low-dimensional manifold space by diffusion maps. Finally, the low-dimensional eigenvectors...
In order to correctly recognize the current state of equipment for preventing equipment farther degradation and occurrence of failure, a new method of equipment degradation state recognition based on wavelet correlation feature scale entropy(WCFSE) and hidden semi-Markov models (HSMM ) was proposed. Firstly, the gathered vibration signal of equipment was processed by the way of the wavelet transform...
Due to the weak energy and nonstationarity, incipient fault characteristic signals are usually submerged by vibration signals of rotary machine and noise. Based on the multi-resolution feature and time-frequency localization feature of Wavelet Transform, a method to extract fault characteristic signals by decomposing them into corresponding time-frequency segmentations is presented. The noise is attenuated,...
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