Health Status Assessment Method of Intelligent Power Plant Equipment Based on Integrated Deep Random Forest Algorithm
To accurately assess the health status of power plant equipment,it is of great significance to ensure the safe and stable production of power plants and improve the safety of equipment operation.Aimed at low prediction accuracy in current power plant e-quipment health assessment methods,an intelligent power plant equipment health assessment method based on the integrated deep random forest algorithm is proposed.Firstly,the power plant equipment health assessment system structure is introduced in detail,and the health assessment data structure and influencing factors are analyzed.Secondly,the equipment evaluation is divided into six different levels,which makes the equipment health analysis more convenient.Then,combined with deep learning and integrated learning technology,an integrated deep random forest algorithm is proposed.Finally,simulation experiments verify the effectiveness of the proposed method.The results show that the evaluation model accuracy of the proposed method can reach up to 97％,and the proposed algorithm has the highest accuracy in health assessment compared with other evaluation methods.
equipment health assessmentdeep random forestintegrated learningintegrated deep forest
NETL
NSTL
万方数据
