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- W3114148990 abstract "In order to realize the monitoring of grinding roller wear state of coal mill in power plant as well as improve the reliability and safety of equipment, it is essential to build a high accuracy model for monitoring wear state of grinding roller. Previous studies have shown that the mill power can represent the wear degree of the grinding roller. Assuming that the voltage of mill and power factor are unchanged, the mill power can be replaced by mill current. In this study, the current model of coal mill is built by collecting historical operation data and LSTM (Long Short-Term Memory) algorithms is adopted. A residual is formed by the predicted value and the actual value, and then the low frequency component obtained by wavelet multi-scale decomposition is used to characterize the wear state trend of grinding roller. The validity of the proposed method is demonstrated by field data of a certain unit. The results verify that the LSTM prediction model has higher prediction accuracy comparing with the traditional BP neural network model. The constructed residual can well reflect the changing trend of roller wear state after wavelet optimal scale decomposition. The proposed method has good engineering practical significance." @default.
- W3114148990 created "2021-01-05" @default.
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- W3114148990 date "2020-10-14" @default.
- W3114148990 modified "2023-10-16" @default.
- W3114148990 title "A Wear Condition Monitoring Model of Coal Mill Grinding Roller Based on LSTM" @default.
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- W3114148990 doi "https://doi.org/10.1145/3434581.3434628" @default.
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