热成像
方位(导航)
红外线的
断层(地质)
稳健性(进化)
振动
卷积神经网络
状态监测
特征提取
小波变换
小波
模式识别(心理学)
人工智能
声学
计算机科学
工程类
地质学
地震学
光学
物理
生物化学
化学
电气工程
基因
作者
Tauheed Mian,Anurag Choudhary,Shahab Fatima
标识
DOI:10.1080/10589759.2022.2118747
摘要
The occurrence of multiple faults is a practical problem in the bearings of rotating machines, and early diagnosis of such issues in an intelligent manner is vital in the era of industry 4.0. The present work investigated various combinations of bearing faults, including dual and multiple fault conditions. Two prevalent fault diagnosis methods were employed: vibration monitoring using time-frequency scalograms extracted through Continuous Wavelet Transform (CWT) and a non-invasive Infrared Thermography (IRT). A 2-D Convolutional Neural Network (CNN) was used to classify various combinations of fault conditions through automated feature extraction. The proposed methodology was validated at two constant speed conditions of 19 Hz and 29 Hz and continuously accelerated and decelerated speed conditions in the range of 19 Hz - 29 Hz. Adequate accuracy was achieved in both dual and multiple fault conditions in the case of vibration-based fault diagnosis, with a range of 99.39 % to 99.97 %. Meanwhile, in the case of proposed IRT-based fault diagnosis, 100 % classification accuracy was achieved for dual and multiple faults in all conditions. These results signify the robustness and reliability of the proposed methodology for dual and multiple fault diagnosis in bearings at constant and varying speed conditions.
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