Compound feature selection and parameter optimization of ELM for fault diagnosis of rolling element bearings

2016 ◽  
Vol 65 ◽  
pp. 556-566 ◽  
Author(s):  
Meng Luo ◽  
Chaoshun Li ◽  
Xiaoyuan Zhang ◽  
Ruhai Li ◽  
Xueli An
Keyword(s):  
Feature Selection ◽  
Fault Diagnosis ◽  
Parameter Optimization ◽  
Rolling Element Bearings ◽  
Rolling Element

A NEW GEARBOX FAULT DIAGNOSIS METHOD BASED ON LUCY–RICHARDSON DECONVOLUTION

2015 ◽  
Vol 39 (3) ◽  
pp. 593-603
Author(s):  
Xinghui Zhang ◽  
Jianshe Kang ◽  
Hongzhi Teng ◽  
Jianmin Zhao
Keyword(s):  
Fault Diagnosis ◽  
Experimental Studies ◽  
Experimental Tests ◽  
Rolling Element Bearings ◽  
Envelope Analysis ◽  
Rolling Element ◽  
Negative Effect ◽  
Diagnosis Method ◽  
Good Identification ◽  
Incipient Fault Diagnosis

Gear and bearing faults are the main causes of gearbox failure. Till now, incipient fault diagnosis of these two components has been a problem and needs further research. In this context, it is found that Lucy–Richardson deconvolution (LRD) proved to be an excellent tool to enhance fault diagnosis in rolling element bearings and gears. LRD’s good identification capabilities of fault frequencies are presented which outperform envelope analysis. This is very critical for early fault diagnosis. The case studies were carried out to evaluate the effectiveness of the proposed method. The results of simulated and experimental studies show that LRD is efficient in alleviating the negative effect of noise and transmission path. The results of simulation and experimental tests demonstrated outperformance of LRD compared to classical envelope analysis for fault diagnosis in rolling element bearings and gears, especially when it is applied to the processing of signals with strong background noise.

Vibration model of rolling element bearings in a rotor-bearing system for fault diagnosis

2013 ◽  
Vol 332 (8) ◽  
pp. 2081-2097 ◽  
Author(s):  
Feiyun Cong ◽  
Jin Chen ◽  
Guangming Dong ◽  
Michael Pecht
Keyword(s):  
Fault Diagnosis ◽  
Rolling Element Bearings ◽  
Vibration Model ◽  
Rolling Element ◽  
Rotor Bearing ◽  
Bearing System

Two-step fault diagnosis framework for rolling element bearings with imbalanced data based on GSA-WELM and GSA-ELM

2017 ◽  
Vol 232 (16) ◽  
pp. 2937-2947 ◽  
Author(s):  
Yuan Lan ◽  
Xiaohong Han ◽  
Weiwei Zong ◽  
Xiaojian Ding ◽  
Xiaoyan Xiong ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Gravitational Search Algorithm ◽  
Imbalanced Data ◽  
Rolling Element Bearing ◽  
Rolling Element Bearings ◽  
Misclassification Cost ◽  
Weighted Extreme Learning Machine ◽  
Rolling Element ◽  
Learning Machine

Rolling element bearings constitute the key parts on rotating machinery, and their fault diagnosis is of great importance. In many real bearing fault diagnosis applications, the number of fault data is much less than the number of normal data, i.e. the data are imbalanced. Many traditional diagnosis methods will get low accuracy because they have a natural tendency to favor the majority class by assuming balanced class distribution or equal misclassification cost. To deal with imbalanced data, in this article, a novel two-step fault diagnosis framework is proposed to diagnose the status of rolling element bearings. Our proposed framework consists of two steps for fault diagnosis, where Step 1 makes use of weighted extreme learning machine in an effort to classify the normal or abnormal categories, and Step 2 further diagnoses the underlying anomaly in detail by using preliminary extreme learning machine. In addition, gravitational search algorithm is applied to further extract the significant features and determine the optimal parameters of the weighted extreme learning machine and extreme learning machine classifiers. The effectiveness of our proposed approach is testified on the raw data collected from the rolling element bearing experiments conducted in our Institute, and the empirical results show that our approach is really fast and can achieve the diagnosis accuracies more than 96%.

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.

scholarly journals Teager Energy Spectrum for Fault Diagnosis of Rolling Element Bearings

2011 ◽  
Vol 305 ◽  
pp. 012129 ◽  
Author(s):  
Zhipeng Feng ◽  
Tianjin Wang ◽  
Ming J Zuo ◽  
Fulei Chu ◽  
Shaoze Yan
Keyword(s):  
Fault Diagnosis ◽  
Energy Spectrum ◽  
Rolling Element Bearings ◽  
Rolling Element ◽  
Teager Energy
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