Abstract
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.
Highlights
In the mechanical system, a basic but important component is roller bearings, whose working performance has great effects on operational efficiency and safety
Based on the multiscale sample entropy (MSE), the feature of the vibration signals can be extracted under various conditions, the eigenvector is regarded as the input of adaptive neuro-fuzzy inference system (ANFIS) for roller bearings fault recognition [17]
Combing with the multiscale base-scale entropy (MBSE), support vector machine (SVM) and particle swarm ion (PSO) methods, a method based on MBSE and random forests (RF) model is presented in this paper
Summary
A basic but important component is roller bearings, whose working performance has great effects on operational efficiency and safety. The value of AE is smaller than the expected value when the length of the data is very short To overcome this disadvantage, an improved method based on AE, called SE, was proposed in [7], AE has been successfully used in fault diagnosis [8]. An improved method based on AE, called SE, was proposed in [7], AE has been successfully used in fault diagnosis [8] It is different from AE and SE, Bandt et al presented PE, a parameter of average entropy, to describe the complexity of a time series [9]. Because the permutation entropy makes use of the order of the values and it is robust under a non-linear distortion of the signal In [11], the authors demonstrated that the computational efficiency of the BSE is good, and applied in physiological signal processing and gear fault
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