The oil spectral analysis data of the classic metal elements contained in the used oil during the longtime test was forecasted by genetic algorithm (GA) and neural network to predict the wear trend of the gas turbine lubricating system.The standard BP network was improved by changing the normalization range of the oil spectral analysis data and adjusting the adaptivity of learning rate,and the weights and thresholds of the improved BP network was optimized by GA.The wear trend forecasting model of a gas turbine lubricating system during its testing period was built,and the potential wear trend was predicted by the welltrained model.The result shows the proposed model is practical and promising in the wear trend prediction of gas turbine lubricating system during the testing period,which can improve the forecasting accuracy of wear fault.
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孙佳斯,佟文伟,郎宏,刘宇佳,何山.燃气轮机润滑系统磨损趋势预测[J].润滑与密封,2017,42(6):113-118. . Wear Trend Forecasting of Gas Turbine Lubricating System[J]. Lubrication Engineering,2017,42(6):113-118.