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- W2367739598 abstract "The condition forecasting technology of large rotating machinery is the key for machinery condition maintenance. A novel system fault trend prediction method based on SVM(support vector machines) for several typical trends is put forward. The SVM model predicting results compared with BP(back propagation) neural network model proves that SVM model is of high predicting accuracy. Based on this, a new condition hybrid forecasting model is presented. This method uses the vibration velocity in time domain and vibration amplitude of the feature frequency in frequency domain as condition trend prediction sensitive factors. The method is used for the experimental system as well as the water injection sets. The good predicting results show that the hybrid method based on SVM has excellent performance of condition trend prediction for mechanical equipment." @default.
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- W2367739598 date "2006-01-01" @default.
- W2367739598 modified "2023-09-27" @default.
- W2367739598 title "STUDY ON LARGE ROTATING MACHINERY CONDITION HYBRID FORCASTING MODEL" @default.
- W2367739598 hasPublicationYear "2006" @default.
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