An Improved Discrete Bat Algorithm Used for System Fault Diagnosis

2017 ◽  
Vol 11 (2) ◽  
pp. 230-235
Author(s):  
Hang Li ◽  
Shoulin Yin
Keyword(s):  
Fault Diagnosis ◽  
Bat Algorithm

scholarly journals Fault diagnosis of bearing based on relevance vector machine classifier with improved binary bat algorithm for feature selection and parameter optimization

2017 ◽  
Vol 9 (1) ◽  
pp. 168781401668529 ◽  
Author(s):  
Sheng-wei Fei
Keyword(s):  
Feature Selection ◽  
Fault Diagnosis ◽  
Parameter Optimization ◽  
Bat Algorithm ◽  
Relevance Vector Machine ◽  
Experimental Results ◽  
Machine Method ◽  
Training Samples ◽  
Kernel Parameter

In this article, fault diagnosis of bearing based on relevance vector machine classifier with improved binary bat algorithm is proposed, and the improved binary bat algorithm is used to select the appropriate features and kernel parameter of relevance vector machine. In the improved binary bat algorithm, the new velocities updating method of the bats is presented in order to ensure the decreasing of the probabilities of changing their position vectors’ elements when the position vectors’ elements of the bats are equal to the current best location’s element, and the increasing of the probabilities of changing their position vectors’ elements when the position vectors’ elements of the bats are unequal to the current best location’s element, which are helpful to strengthen the optimization ability of binary bat algorithm. The traditional relevance vector machine trained by the training samples with the unreduced features can be used to compare with the proposed improved binary bat algorithm–relevance vector machine method. The experimental results indicate that improved binary bat algorithm–relevance vector machine has a stronger fault diagnosis ability of bearing than the traditional relevance vector machine trained by the training samples with the unreduced features, and fault diagnosis of bearing based on improved binary bat algorithm–relevance vector machine is feasible.

Corrigendum to “Reliable fault diagnosis for incipient low-speed bearings using feature analysis based on a binary bat algorithm” [Inform. Sci. 294 (2015) 423–438]

2015 ◽  
Vol 311 ◽  
pp. 205-206 ◽  
Author(s):  
Myeongsu Kang ◽  
Jaeyoung Kim ◽  
Jong-Myon Kim ◽  
Andy C.C. Tan ◽  
Eric Y. Kim ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Bat Algorithm ◽  
Feature Analysis ◽  
Low Speed

scholarly journals Effective Rotor Fault Diagnosis Model Using Multilayer Signal Analysis and Hybrid Genetic Binary Chicken Swarm Optimization

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 487
Author(s):  
Chun-Yao Lee ◽  
Guang-Lin Zhuo
Keyword(s):  
Fault Diagnosis ◽  
Evolutionary Algorithms ◽  
Signal Analysis ◽  
Bat Algorithm ◽  
Support Vector ◽  
Binary Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Raw Data ◽  
Diagnosis Model ◽  
Chicken Swarm Optimization

This article proposes an effective rotor fault diagnosis model of an induction motor (IM) based on local mean decomposition (LMD) and wavelet packet decomposition (WPD)-based multilayer signal analysis and hybrid genetic binary chicken swarm optimization (HGBCSO) for feature selection. Based on the multilayer signal analysis, this technique can reduce the dimension of raw data, extract potential features, and remove background noise. To compare the validity of the proposed HGBCSO method, three well-known evolutionary algorithms are adopted, including binary-particle swarm optimization (BPSO), binary-bat algorithm (BBA), and binary-chicken swarm optimization (BCSO). In addition, the robustness of three classifiers including the decision tree (DT), support vector machine (SVM), and naive Bayes (NB) was compared to select the best model to detect the rotor bar fault. The results showed that the proposed HGBCSO algorithm can obtain better global exploration ability and a lower number of selected features than other evolutionary algorithms that are adopted in this research. In conclusion, the proposed model can reduce the dimension of raw data and achieve high robustness.

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