Induction Motor Bearing Fault Classification Using Extreme Learning Machine Based on Power Features

2021 ◽  
Author(s):  
Niloy Sikder ◽  
Abu Shamim Mohammad Arif ◽  
M. M. Manjurul Islam ◽  
Abdullah-Al Nahid
Keyword(s):  
Induction Motor ◽  
Extreme Learning Machine ◽  
Fault Classification ◽  
Bearing Fault ◽  
Learning Machine ◽  
Motor Bearing

Induction motor bearing fault classification using WPT, PCA and DSVM

2018 ◽  
Vol 35 (5) ◽  
pp. 5147-5158 ◽  
Author(s):  
Sudhir Agrawal ◽  
V.K. Giri ◽  
A.N. Tiwari
Keyword(s):  
Induction Motor ◽  
Fault Classification ◽  
Bearing Fault ◽  
Motor Bearing

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.

scholarly journals Signal Processing Methods for Identification of Induction Motor Bearing Fault

2019 ◽  
Vol 8 (3) ◽  
pp. 143-151
Keyword(s):  
Signal Processing ◽  
Feature Extraction ◽  
Induction Motor ◽  
Dominant Mode ◽  
Processing Methods ◽  
Vibration Signals ◽  
Bearing Fault ◽  
Vibration Signal Analysis ◽  
Signal Processing Methods ◽  
Motor Bearing

To diagnose early faults as soon as possible, the feature extraction of vibration signals is very important in real engineering applications. Recently, the advanced signal processing-based weak feature extraction method has been becoming a hot research topic. The dominant mode of failure in rolling element bearings is spalling of the races or the rolling elements. Localized defects generate a series of impact vibrations every time whenever running roller passes over the surface of a defect. Therefore, vibration analysis is a conventional method for bearing fault detection. However, the measured vibration signals of rotating machinery often present nonlinear and non-stationary characteristics. This paper deals with the diagnosis of induction motor bearing based on vibration signal analysis. It provides a comparative study between traditional signal processing methods, such as Power Spectrum, Short Time Fourier Transform, Wavelet Transform, and Hilbert Transform. Performances of these techniques are assessed on real vibration data and compared for healthy and faulty bearing.

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