本研究提出以倒傳遞類神經網路建立風機功率預測模型，並以預測值與實際值之誤差，定義風機健康評估指標，建立風機預兆式診斷及健康管理系統。本研究以林口發電廠4號風機為研究對象，利用4號風機於2012年至2015年完整4年之SACDA資料，實現風機健康之診斷。再利用Elman類神經網路進行風機健康評估指標衰退趨勢之預測，評估風機健康剩餘使用壽命。研究結果指出，定義健康評估指標值為0.15時，林口4號風機之健康剩餘使用壽命約為15年。本研究藉由功率預測演算法及健康指標預測演算法，達成健康診斷及健康剩餘壽命預測兩部分，建立風機預兆式診斷及健康管理系統，做為風機維修之決策依據。

This research proposed a back-propagation neural network algorithm to establish Wind Power Forecasting model and implement the Health Assessment of wind turbine by Health Index which is defined using the error between forecast power and actually power. Based on wind turbine NO.4 in Linkou of Taipower, the system of prognostics and health management was set up with the data collected by the supervisory control and data acquisition (SCADA) system fom 2012 to 2015. Then the Elman neural network was used to get the degradation of health index. Finally, health remaining useful life time of the wind turbine was predicted from the SCADA data. The analysis shows that the health remaining useful life time of the wind turbine NO.4 in Linkou of Taipower is about 15 years if the health index is defined as 0.15. The health assessment and health remaining useful life time of the wind turbine can be forecasted by the proposed neural network prognostics model and the criteria of health index. The developed prognostics and health management model can be used for wind turbine maintenance.