Journal ArticleDOI
Autocorrelation Aided Random Forest Classifier-Based Bearing Fault Detection Framework
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TLDR
The proposed autocorrelation aided feature extraction method has yielded very high accuracy in identifying different bearing defects which can be practically implemented for automated bearing fault detection of induction motors.Abstract:
Rolling bearing defects in induction motors are usually diagnosed using vibration signal analysis. For accurate detection of rolling bearing defects, appropriate feature extraction from vibration signals is necessary, failure of which may lead to incorrect interpretation. Considering the above fact, this article presents an autocorrelation aided feature extraction method for diagnosis of rolling bearing defects. To this end, the vibration signals of healthy as well as different faulty bearings were recorded using accelerometers and autocorrelation of the respective vibration signals were done to examine their self-similarity in time scale. Following this, several statistical, hjorth as well as non-linear features were extracted from the respective vibration correlograms and were subjected to feature reduction using recursive feature elimination technique. The dimensionally reduced top ranked feature vectors were subsequently fed to a random forest classifier for classification of vibration signals. A large number of experiments were carried out for (i) three different fault diameters at (ii) four different shaft speeds and also at (iii) two different sampling frequencies. Besides, for each condition, six binary class and one multiclass classification problem is also addressed in this paper, resulting in a total 112 different classification tasks. It was observed that the proposed method has yielded very high accuracy in identifying different bearing defects which can be practically implemented for automated bearing fault detection of induction motors.read more
Citations
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Journal ArticleDOI
Effective Random Forest-Based Fault Detection and Diagnosis for Wind Energy Conversion Systems
Radhia Fezai,Khaled Dhibi,Majdi Mansouri,Mohamed Trabelsi,Mansour Hajji,Kais Bouzrara,Hazem Nounou,Mohamed Nounou +7 more
TL;DR: This paper proposes four improved RF methods that aim to reduce at first the amount of the training data and select the first kernel principal components using different kernel principal component analysis (PCA) based dimensionality reduction schemes.
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Discriminative Dictionary Learning-Based Sparse Classification Framework for Data-Driven Machinery Fault Diagnosis
TL;DR: Wang et al. as mentioned in this paper proposed a novel discriminative dictionary learning based sparse classification (DDL-SC) framework for data-driven machinery fault diagnosis, which can jointly learn the dictionary for sparse representation and the linear classifier for pattern recognition.
Journal ArticleDOI
FaultNet: A Deep Convolutional Neural Network for Bearing Fault Classification
TL;DR: In this paper, a convolutional neural network FaultNet was proposed to detect bearing faults with a high degree of accuracy, the distinguishing factor of this work is the idea of channels proposed to extract more information from the signal, they have stacked the Mean and Median channels to the raw signal and extracted more useful features to classify the signals with greater accuracy.
Journal ArticleDOI
A Novel Fault Diagnosis of Uncertain Systems Based on Interval Gaussian Process Regression: Application to Wind Energy Conversion Systems
Majdi Mansouri,Radhia Fezai,Mohamed Trabelsi,Mansour Hajji,Mohamed-Faouzi Harkat,Hazem Nounou,Mohamed Nounou,Kais Bouzrara +7 more
TL;DR: A novel Interval Gaussian Process Regression-based Random Forest technique for diagnosing uncertain WEC systems and is characterized by a better handling of WEC system uncertainties such as wind variability, noise, measurement errors, which leads to improved fault classification accuracy.
Journal ArticleDOI
An improved random forest algorithm and its application to wind pressure prediction
TL;DR: The results show that the improved RF can achieve good results in predicting the mean and fluctuating wind pressure coefficients of high‐rise buildings, and its relative error for each measurement point is basically controlled at 5%, which is acceptable in engineering terms.
References
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TL;DR: Various ways of performing dimensionality reduction on high-dimensional microarray data are summarised to provide a clearer idea of when to use each one of them for saving computational time and resources.
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Motor Bearing Fault Detection Using Spectral Kurtosis-Based Feature Extraction Coupled With K -Nearest Neighbor Distance Analysis
TL;DR: The method is able to detect incipient faults and diagnose the locations of faults under masking noise, and provides a health index that tracks the degradation of faults without missing intermittent faults.
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Diagnosis of Bearing Faults in Induction Machines by Vibration or Current Signals: A Critical Comparison
TL;DR: In this paper, a simple and effective signal processing technique for both current and vibration signals, and a theoretical analysis of the physical link between faults, modeled as a torque disturbance, and current components, are presented.
Journal ArticleDOI
SVM-RFE With MRMR Filter for Gene Selection
TL;DR: The support vector machine recursive feature elimination (SVM-RFE) method for gene selection is enhanced by incorporating a minimum-redundancy maximum-relevancy (MRMR) filter, which provides a framework for combining filter methods and wrapper methods of gene selection.
Journal ArticleDOI
Multi-fault classification based on wavelet SVM with PSO algorithm to analyze vibration signals from rolling element bearings
TL;DR: The proposed hybrid intelligent fault detection and classification method can reliably identify different fault patterns of rolling element bearings based on the vibration signals and can achieve a greater accuracy than the commonly used SVM.