Zhang Xu
1State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China;Huang Yixiang
1State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200241, China;Zhang Xudong
1State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200242, China;Liu Chengliang
1State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200243, China;Xiao Dengyu
1State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200244, China;Shan Zenghai
2Xuzhou Heavy Machinery Limited Company and State Key Laboratory of Advanced Manufacturing Machinery Intelligent Manufacturing, Xuzhou 221004, ChinaTH17;TN9
Aiming at the problem that it is difficult to evaluate the overall health status of the crane rotary system under realtime conditions, a health evaluation method for the rotary system combining Laplacian Eigenmaps and KullbackLeibler distance is proposed. After collecting the multidimensional signal of rotary system, the Laplacian eigenmaps and Random Forest are used to reduce noise and dimensionality of the signal. Then combined with the working principle of the rotary system, the health performance of the rotary system is characterized by Gaussian kernel density estimation. The KullbackLeibler distance between different rotary system is calculated by probability density to characterize the health performance of the rotary system. The test results show that this method can avoid the noise interference of the original data and the health assessment results of the rotary system are consistent with the expert assessment results.
