A method based on multiscale base-scale entropy and random forests for roller bearings faults diagnosis

• Main Authors: Fan Xu, Yan Jun Fang, Zhou Wu, Jia Qi Liang
• Format: Article
• Language: English
• Published: JVE International 2018-02-01
• Series: Journal of Vibroengineering
• Subjects: roller bearings; fault diagnosis; multiscale base-scale entropy; random forests
• Online Access: https://www.jvejournals.com/article/17153

A method based on multiscale base-scale entropy (MBSE) and random forests (RF) for roller bearings faults diagnosis is presented in this study. Firstly, the roller bearings vibration signals were decomposed into base-scale entropy (BSE), sample entropy (SE) and permutation entropy (PE) values by using MBSE, multiscale sample entropy (MSE) and multiscale permutation entropy (MPE) under different scales. Then the computation time of the MBSE/MSE/MPE methods were compared. Secondly, the entropy values of BSE, SE, and PE under different scales were regarded as the input of RF and SVM optimized by particle swarm ion (PSO) and genetic algorithm (GA) algorithms for fulfilling the fault identification, and the classification accuracy was utilized to verify the effect of the MBSE/MSE/MPE methods by using RF/PSO/GA-SVM models. Finally, the experiment result shows that the computational efficiency and classification accuracy of MBSE method are superior to MSE and MPE with RF and SVM.