Application of Spectral Kurtosis and Improved Extreme Learning Machine for Bearing Fault Classification

2019 ◽  
Vol 68 (11) ◽  
pp. 4222-4233 ◽  
Author(s):  
Sandeep S. Udmale ◽  
Sanjay Kumar Singh
Keyword(s):  
Extreme Learning Machine ◽  
Fault Classification ◽  
Bearing Fault ◽  
Spectral Kurtosis ◽  
Learning Machine

scholarly journals A Novel Faults Diagnosis Method for Rolling Element Bearings Based on ELCD and Extreme Learning Machine

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Mingliang Liang ◽  
Dongmin Su ◽  
Daidi Hu ◽  
Mingtao Ge
Keyword(s):  
Extreme Learning Machine ◽  
Rolling Bearing ◽  
Local Characteristic ◽  
Support Vector ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Evaluation Approach ◽  
Diagnosis Method ◽  
Learning Machine ◽  
Intrinsic Scale

A rolling bearing fault diagnosis method based on ensemble local characteristic-scale decomposition (ELCD) and extreme learning machine (ELM) is proposed. Vibration signals were decomposed using ELCD, and numerous intrinsic scale components (ISCs) were obtained. Next, time-domain index, energy, and relative entropy of intrinsic scale components were calculated. According to the distance-based evaluation approach, sensitivity features can be extracted. Finally, sensitivity features were input to extreme learning machine to identify rolling bearing fault types. Experimental results show that the proposed method achieved better performance than support vector machine (SVM) and backpropagation (BP) neural network methods.

scholarly journals Test Generation Algorithm for Fault Detection of Analog Circuits Based on Extreme Learning Machine

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Jingyu Zhou ◽  
Shulin Tian ◽  
Chenglin Yang ◽  
Xuelong Ren
Keyword(s):  
Extreme Learning Machine ◽  
Analog Circuits ◽  
Test Generation ◽  
Cost Effective ◽  
Fault Classification ◽  
The Novel ◽  
Analog Device ◽  
Generation Algorithm ◽  
New Process ◽  
Learning Machine

This paper proposes a novel test generation algorithm based on extreme learning machine (ELM), and such algorithm is cost-effective and low-risk for analog device under test (DUT). This method uses test patterns derived from the test generation algorithm to stimulate DUT, and then samples output responses of the DUT for fault classification and detection. The novel ELM-based test generation algorithm proposed in this paper contains mainly three aspects of innovation. Firstly, this algorithm saves time efficiently by classifying response space with ELM. Secondly, this algorithm can avoid reduced test precision efficiently in case of reduction of the number of impulse-response samples. Thirdly, a new process of test signal generator and a test structure in test generation algorithm are presented, and both of them are very simple. Finally, the abovementioned improvement and functioning are confirmed in experiments.

A sequential feature extraction method based on discrete wavelet transform, phase space reconstruction, and singular value decomposition and an improved extreme learning machine for rolling bearing fault diagnosis

2017 ◽  
Vol 232 (6) ◽  
pp. 635-649 ◽  
Author(s):  
DZ Li ◽  
X Zheng ◽  
QW Xie ◽  
QB Jin
Keyword(s):  
Fault Diagnosis ◽  
Phase Space ◽  
Extreme Learning Machine ◽  
Rolling Bearing ◽  
Singular Value ◽  
Discrete Wavelet ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Learning Machine ◽  
Value Decomposition

A novel fault diagnosis approach based on a combination of discrete wavelet transform, phase space reconstruction, singular value decomposition, and improved extreme learning machine is presented in rolling bearing fault identification and classification. The proposed method provides proper solutions for improving the accuracy of faults classification. To achieve this goal, initial signals are divided into sub-band wavelet coefficients using discrete wavelet transform. Then, each of sub-band is mapped into three-dimensional space using the phase space reconstruction method to completely describe characteristics in the high dimension. Thereafter, singular values are calculated by singular value decomposition method, which demonstrate crucial variances in original vibration signal. Lastly, an improved extreme learning machine is adopted as a classifier for fault classification. The proposed method is applied to the rolling bearing fault diagnosis with non-linear and non-stationary characteristics. Based on outputs of the improved extreme learning machine, the working condition and fault location could be determined accurately and quickly. Achieved results, compared with other schemes, show that the proposed scheme in this article can be regarded as an effective and reliable method for rolling bearing fault diagnosis.

scholarly journals Fault Classification System for Switchgear CBM from an Ultrasound Analysis Technique Using Extreme Learning Machine

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6279
Author(s):  
Sanuri Ishak ◽  
Chong Tak Yaw ◽  
Siaw Paw Koh ◽  
Sieh Kiong Tiong ◽  
Chai Phing Chen ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Classification System ◽  
Fault Classification ◽  
Surface Discharge ◽  
Test Results ◽  
Analysis Technique ◽  
Artificial Neural Network Ann ◽  
Learning Machine ◽  
Interface Screen ◽  
Wrong Interpretation

Currently, the existing condition-based maintenance (CBM) diagnostic test practices for ultrasound require the tester to interpret test results manually. Different testers may give different opinions or interpretations of the detected ultrasound. It leads to wrong interpretation due to depending on tester experience. Furthermore, there is no commercially available product to standardize the interpretation of the ultrasound data. Therefore, the objective is the correct interpretation of an ultrasound, which is one of the CBM methods for medium switchgears, by using an artificial neural network (ANN), to give more accurate results when assessing their condition. Information and test results from various switchgears were gathered in order to develop the classification and severity of the corona, surface discharge, and arcing inside of the switchgear. The ultrasound data were segregated based on their defects found during maintenance. In total, 314 cases of normal, 160 cases of the corona, 149 cases of tracking, and 203 cases of arcing were collected. Noise from ultrasound data was removed before uploading it as a training process to the ANN engine, which used the extreme learning machine (ELM) model. The developed AI-based switchgear faults classification system was designed and incorporated with the feature of scalability and can be tested and replicated for other switchgear conditions. A customized graphical user interface (GUI), Ultrasound Analyzer System (UAS), was also developed, to enable users to obtain the switchgear condition or classification output via a graphical interface screen. Hence, accurate decision-making based on this analysis can be made to prioritize the urgency for the remedial works.

Multi-fault classification based on the two-stage evolutionary extreme learning machine and improved artificial bee colony algorithm

2013 ◽  
Vol 228 (10) ◽  
pp. 1797-1807 ◽  
Author(s):  
Jian Xiao ◽  
Jianzhong Zhou ◽  
Chaoshun Li ◽  
Han Xiao ◽  
Weibo Zhang ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Artificial Bee Colony ◽  
Fault Classification ◽  
Feed Forward Neural Network ◽  
Two Stage ◽  
Node Number ◽  
Generalization Performance ◽  
Bee Colony ◽  
Hidden Biases ◽  
Learning Machine

Extreme Learning Machine (ELM) is a novel single-hidden-layer feed forward neural network with fast learning speed and better generalization performance compared with the traditional gradient-based learning algorithms. However, ELM has two issues: the hidden node number of ELM needs to be predefined and the random determination of the input weights and hidden biases lead to ill-condition problem. In this paper, a two-stage evolutionary extreme learning machine (TSE-ELM) algorithm was proposed to overcome the drawbacks of original ELM, which used an improved artificial bee colony (ABC) algorithm to optimize the input weights and hidden biases. The proposed TSE-ELM algorithm was applied on the UCI benchmark datasets and rolling bearing fault diagnosis. The numerical experimental results demonstrated that TSE-ELM had an improved generalization performance than traditional ELM and other evolutionary ELMs.

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