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国外文献 > 1.轴承 > 7. 智能方法与计算 > 机器学习

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标 题： A comparative Analysis of 1D Convolutional Neural Networks for Bearing Fault Diagnosis
一维卷积神经网络在轴承故障诊断中的比较分析
作 者： Aydil Bapir
Ilhan Aydin
关键词： Variational mode decomposition (VMD)  [变分模分解（VMD）]
1-D CNN  [ 1-D CNN]
Induction motors  [ 感应电动机]
Bearing faults  [ 轴承故障]
Deep learning  [ 深度学习]
LSTM  [ LSTM]
CapsuleNet  [ 胶囊网络]
出版年： 2022
文集名： 2022 International Conference on Decision Aid Sciences and Applications (DASA 2022)
2022年决策辅助科学与应用国际会议（DASA 2022）
会议名： 2022 International Conference on Decision Aid Sciences and Applications (DASA 2022)
2022年决策辅助科学与应用国际会议（DASA 2022）
届 次：
摘 要： In many industrial applications, bearing monitoring is ranked as a crucial duty for condition-based maintenance. It enables the avoidance of unplanned maintenance while lowering the cost. For this objective, a variety of strategies have been devised to assure accurate and effective monitoring. This study offers a One-Dimensional Convolution Neural Network (1D-CNN) that is used to diagnose and identify early bearing faults based on Variational Mode Decomposition (VMD). Methods such as VMD-based feature extraction for defect detection are followed by multi-scale feature extraction for classification and diagnosis using CNN and pooling layers. Bearing fault detection and diagnosis are evaluated using the Case Western Reserve University experimental dataset to determine its robustness and performance. Demodulation techniques and machine learning algorithms are also contrasted for their ability to identify and diagnose the fault. A VMD filter and 1D-CNN approach for monitoring bearing fault are clearly promising, according to the results obtained from the experiments.

在许多工业应用中，轴承监测被视为基于状态的维护中的关键任务。它有助于避免非计划性维护并降低运营成本。为实现这一目标，人们提出了多种策略以确保监测的准确性和有效性。本研究提出了一种基于变分模态分解（VMD）的一维卷积神经网络（1D-CNN），用于诊断和识别早期轴承故障。该方法首先采用基于VMD的特征提取技术进行缺陷检测，随后利用卷积神经网络（CNN）和池化层实现多尺度特征提取，用于分类与诊断。通过使用西大大学实验数据集对轴承故障的检测与诊断进行评估，以验证其鲁棒性和性能。同时，还对比了不同解调技术和机器学习算法在识别和诊断故障方面的表现。实验结果表明，基于VMD滤波器与1D-CNN的轴承故障监测方法具有明显潜力。
中文内容为网络AI翻译，专业术语未必准确，仅供参考。

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