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- W2763048134 abstract "High Pressure Liquefied Natural Gas Pump (HP-LNG Pump) is important equipment in the LNG receiving terminal process, and plays a key role in determining the total supply capacity of natural gas in LNG terminal. Therefore, the condition monitoring, fault diagnostics and prognostics technologies are applied to implement the CBM (Condition Based Maintenance) strategy for HP-LNG pumps, which can support for appropriate maintenance decision. Currently, a number of valuable diagnostic models and methods have been proposed in machine diagnostics. However, most intelligent diagnostic models have generally validated using an experimental data, and still not focus on industrial real data because of the complex nature in real industry. In this paper, intelligent fault diagnostic performances using three conventional classification algorithms such as SVM (Support Vector Machines), k-NN (k-Nearest Neighbor) and Fuzzy-ARTMAP have been evaluated for the CBM decision of HP-LNG pump. Comparative results indicate that the fault classification using SVM provides more accurate performance compared to other classification methods for HP-LNG pump." @default.
- W2763048134 created "2017-10-20" @default.
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- W2763048134 date "2017-10-04" @default.
- W2763048134 modified "2023-09-26" @default.
- W2763048134 title "A Comparison Study on Intelligent Fault Diagnostics for Condition Based Maintenance of High-Pressure LNG Pump" @default.
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- W2763048134 doi "https://doi.org/10.1007/978-3-319-62274-3_10" @default.
- W2763048134 hasPublicationYear "2017" @default.
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