Application of KTA-KELM in Fault Diagnosis of Rolling Bearing - Zhuo Wang; Wenjun Zhao; Tao Ma; Zhijun Li; Bo Qin (Jixie chuandong)
In the process of data-driven rolling bearing state identification model construction，the improper selection of the radial width parameter σ of the Gaussian kernel function in the Kernel Extreme Learning Machine（KELM）algorithm is very easy to cause poor classification accuracy. A method for identifying the state of rolling bearings based on the kernel arrangement preferred kernel parameter σ is proposed. Firstly，the rolling bearing vibration signal is decomposed by Ensemble Local Mean Decomposition（ELMD）and its energy entropy and permutation entropy are calculated to construct a high-dimensional eigenvector set. Then，the Kernel Target Alignment（KTA） parameters of maximum KTA value Ai and the kernel parameter σi are initialized, and the different kernel parameter values are adjusted by judging the distance between the kernel matrix and the ideal target matrix，so as to obtain the minimum corresponding maximum kernel arrangement value when the kernel matrix distance is obtained，and the kernel parameter at this time is optimal. Finally，the high-dimensional feature vector set of the above rolling bearing is used as input to learn the KTA-KELM algorithm, the state recognition model of rolling bearing is built based on KTA-KELM algorithm. The simulation results show that compared with KELM and ELM，the KTA-KELM algorithm improves the accuracy of rolling bearing state recognition from 92.5% and 90% to 98.75%， which is increase 6.25% and 8.75%.