Considering the Influence of Kernel Function of Transfer Component Analysis and its Application in Variable Working Condition of Gearbox Fault Diagnosis
DUAN Li-xiang,XIE Jun-yao,WANG Kai,WANG Jin-jiang
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School of Mechanical and Transportation Engineering, China University of Petroleum, Beijing, China, 102249
In view of the low reusability of monitoring data, the influencing of complex working condition and failure of trained model, Transfer Component Analysis (TCA) is introduced to solve the problem of equipment fault diagnosis in variable working conditions. TCA methoddecreases the distribution difference of train sample and test sample, by utilizing kernel function to map the feature of two samples to latent space. Besides, the effect of four different kernel functions is compared in TCA algorithm and transfer degree index KL divergence (Kullback-leibler divergence) is introduced to measure the distribution difference degree of train samples and test sample. According to the simulation analysis and experiment verification, compared with traditional machine learning methods, TCA performs better in reduction the distribution difference of two samples and improving gearbox diagnose accuracy, especially the Gaussian kernel function.
DUAN Li-xiang,XIE Jun-yao,WANG Kai,WANG Jin-jiang.
Considering the Influence of Kernel Function of Transfer Component Analysis and its Application in Variable Working Condition of Gearbox Fault Diagnosis[J]. Journal of Vibration and Shock, 2017, 36(10): 104-108
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