A Model for Prediction of Outer Race Defects of Rolling Contact Bearing based on Vibration Data Using Machine Learning Algorithms

Q3 Engineering
Kunal Kumar Gupta, S. M. Muzakkir
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引用次数: 0

Abstract

The detection of bearing defects while the machinery is in use is essential for predicting the incipient failure and thereby providing an opportunity to take remedial measures for preventing the costly downtime and ensuring the safe and efficient operation of rotating machinery. With the increasing availability of vibration sensor data and the development of machine learning techniques, the ML methods have become a popular approach for automated fault diagnosis in bearings. In this paper, an attempt has been made to detect the faults in the outer race of bearing using different ML algorithms. An experimental setup has been designed and fabricated to conduct experiments on healthy and faulty bearings and the vibration signals were captured. The captured vibration signals were directly employed as images for training the ML algorithms without the need for conducting the spectral analysis. Six machine learning algorithms, namely, Linear Regression (LR), Decision Tree (DTR), KNN Regression (KNNR), Random Forest Regression (RFR), Convolution Neural Network (CNN), Naive Bayes (NB) were separately applied to classify the location of defects within the outer race of the ball bearing. The accuracy table are used to find the best suitable algorithm for the predictions. The methodology includes data preprocessing techniques, network architectures, training strategies, and evaluation metrics. It has been established that the use of ML technique is very effective in detecting the bearing defects and CNN is able to achieve 100% accuracy.
基于振动数据的滚动接触轴承外圈缺陷预测模型(使用机器学习算法
在机械使用过程中检测轴承缺陷对于预测故障苗头至关重要,从而提供采取补救措施的机会,防止代价高昂的停机时间,确保旋转机械的安全高效运行。随着振动传感器数据可用性的增加和机器学习技术的发展,ML 方法已成为轴承故障自动诊断的流行方法。本文尝试使用不同的 ML 算法检测轴承外圈的故障。本文设计并制作了一个实验装置,对健康轴承和故障轴承进行了实验,并采集了振动信号。采集到的振动信号被直接用作训练 ML 算法的图像,而无需进行频谱分析。六种机器学习算法,即线性回归 (LR)、决策树 (DTR)、KNN 回归 (KNNR)、随机森林回归 (RFR)、卷积神经网络 (CNN)、奈夫贝叶斯 (NB),被分别应用于球轴承外圈缺陷位置的分类。准确率表用于找出最适合预测的算法。该方法包括数据预处理技术、网络架构、训练策略和评估指标。结果表明,使用 ML 技术能非常有效地检测轴承缺陷,而 CNN 则能达到 100% 的准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Tribology in Industry
Tribology in Industry Engineering-Mechanical Engineering
CiteScore
2.80
自引率
0.00%
发文量
47
审稿时长
8 weeks
期刊介绍: he aim of Tribology in Industry journal is to publish quality experimental and theoretical research papers in fields of the science of friction, wear and lubrication and any closely related fields. The scope includes all aspects of materials science, surface science, applied physics and mechanical engineering which relate directly to the subjects of wear and friction. Topical areas include, but are not limited to: Friction, Wear, Lubricants, Surface characterization, Surface engineering, Nanotribology, Contact mechanics, Coatings, Alloys, Composites, Tribological design, Biotribology, Green Tribology.
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