Health Status Diagnosis of Hydraulic Pump Based on Multi-information Fusion Based on Cascade Forest Model
Due to the complex characteristics of hydraulic pump operation,using a single sensor for detection has the problem of low fault identification rate.In order to improve the fault diagnosis ability of hydraulic pumps under complex conditions,a multi-sensor information fusion health state diagnosis method based on multi-particle cascade forest(gcForest)algorithm is designed.In this paper,the multi-granularity classification method of deep neural network is introduced into the forest classifier,and the traditional structure and complete random forest classifier are included in each level of cascade forest.The experimental platform is tested and analyzed.The research results show that the accuracy of fault diagnosis is up to 99.6％,which is especially suitable for high-dimensional important feature extraction.The choice of pressure and flow characteristics as indicators cannot reach the ideal diagnostic results.The prediction accuracy and recall rates are higher by combining the temperature and flow parameters.The selection of pressure,flow and temperature combination achieves almost 100％diagnostic accuracy.
hydraulic pumpinformation fusionmulti-granularity cascade forest algorithmhealth status diagnosis
NETL
NSTL
万方数据
