Feature-based performance of SVM and KNN classifiers for diagnosis of rolling element bearing faults

Jamil, Mohd Atif; Khan, Md Asif; Khanam, Sidra

doi:10.21595/vp.2021.22307

Cited by 20 publications

(3 citation statements)

References 13 publications

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“…This algorithm is used to perform distance analysis on each new data sample to see if it belongs to a particular fault category. 29 This algorithm assumes that related entities are close to one another. KNN determines the distance between two points using multiple techniques, such as Euclidian 30 and Manhattan, 31 based on the idea of similarity based on proximity or distance.…”

Section: K-nearest Neighbors (Knn)mentioning

confidence: 99%

Fault Classification of Rolling Element Bearing in Machine Learning Domain

Jamil¹,

Khanam²

2022

The International Journal of Acoustics and Vibration

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Rolling element bearings are crucial components of rotating machinery used in various industries, including aerospace, navigation, machine tools, etc. Therefore, it is essential to establish suitable techniques for condition monitoring and fault diagnosis of bearings to avoid breakdowns and damages during operation for overall industrial sustainability. Vibration-based condition monitoring has been the most employed technique in this perspective. Many researchers have investigated the vibration response of rolling element bearings having inner race defects, outer race defects, or rolling element defects using conventional techniques in past decades. However, Machine Learning (ML) has emerged as another way of bearing fault diagnosis. In this work, fault signatures of ball bearings are classified using a total of 6 (with 24 subcategories) ML models, and a comparative performance of these models is presented. The ML classifiers are trained with extracted time-domain and frequency-domain features using open-source Case Western Reserve University (CWRU) bearing data. Two datasets of different sample size and number of samples of vibration data corresponding to a healthy ball bearing, a defective bearing with inner race defect, a ball defect, and an outer race defect, running at a particular set of working conditions, are considered. The accuracy of ML models is compared to identify the best model for classifying the faults under consideration.

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Section: K-nearest Neighbors (Knn)mentioning

confidence: 99%

Fault Classification of Rolling Element Bearing in Machine Learning Domain

Jamil¹,

Khanam²

2022

The International Journal of Acoustics and Vibration

Self Cite

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“…Deep Residual Shrinkage Network (DRSN) is a new improvement on Deep Residual Network (ResNet). It replaces the weighting of each feature channel in SENet with the soft threshold of each feature channel, and introduces the soft threshold into the network structure of ResNet as a nonlinear layer, so as to improve the learning effect of the deep learning method on the useful features of noisy data or complex data [6][7].…”

Section: The Key Technologymentioning

confidence: 99%

Research on sub-health monitoring of equipment based on industrial big data technology

Zhu¹,

Ma²,

Li³

et al. 2022

Vib. proced.

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Aiming at the problems of traditional equipment fault diagnosis, such as poor real-time performance, low efficiency and strong subjectivity, a method of industrial equipment sub-health status monitoring based on big data technology was proposed. Firstly, the characteristic engineering of vibration signals is formed by preprocessing vibration signals collected by sensors located near the bearing shaft. Then, the deep residual shrinkage network DRSN was used to build the classification model. By tuning the model parameters, the optimal sub-health status identification model of industrial equipment was obtained. Finally, the accuracy, robustness and generalization of the model were verified. Experimental results show that this method has better generalization performance and can significantly improve the accuracy of industrial equipment fault diagnosis, which has important theoretical value and practical significance to solve the sub-health problems of industrial equipment.

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“…Within the ambit of support vector regression, the Kernel function assumes a pivotal role, and it can manifest in three distinct types: linear, polynomial, or radial basis function (RBF) Al-Mukhtar[21]. The RBF is expressed by Equation 1 below Al-Mukhtar[22]. Here, x and y represent different dosage measurements of arbitrary inputs.…”

mentioning

confidence: 99%

Enhancing Wind Tunnel Computational Simulations of Finite Element Analysis Using Machine Learning-Based Algorithms

Al-Haddad,

Jaber,

Ibraheem

et al. 2023

ETJ

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Feature-based performance of SVM and KNN classifiers for diagnosis of rolling element bearing faults

Cited by 20 publications

References 13 publications

Fault Classification of Rolling Element Bearing in Machine Learning Domain

Fault Classification of Rolling Element Bearing in Machine Learning Domain

Research on sub-health monitoring of equipment based on industrial big data technology

Enhancing Wind Tunnel Computational Simulations of Finite Element Analysis Using Machine Learning-Based Algorithms

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