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国外文献 > 1.轴承 > 7. 智能方法与计算 > 神经网络

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标 题： A Deep Residual Convolutional Neural Network based Bearing Fault Diagnosis with Multi-Sensor Data
基于多传感器数据的深度残差卷积神经网络轴承故障诊断
作 者： Guangxing Niu
Enhui Liu
Bin Zhang
Michael Golda
Stephen Mastro
关键词： Bearing  [轴承]
Convolutional neural network  [ 卷积神经网络]
Multi-Sensor  [ 多传感器]
Deep residual convolutional neural network  [ 深度残差卷积神经网络]
出版年： 2021
会议名： 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS 2021)
第四届IEEE国际工业网络物理系统会议（ICPS 2021）
届 次： 4th
摘 要： Accurate bearing fault diagnosis is essential and significant for the safety and reliability of industrial rotating systems. To monitor the health condition of bearings in full scales, multi-sensors are always assembled in different locations of bearings. The data from different types or different locations of sensors have different sensitivity for fault diagnosis and localization, which makes feature extraction from multi-source data is critical for diagnosis. Deep learning based approaches have been widely used in bearing fault diagnosis, and have achieved significant successes. Deep residual convolutional neural network, as an improved structure of convolutional neural networks, has powerful learning ability when dealing with a deeper network. To improve the training efficiency and accuracy of diagnosis and make better use of bearing vibration data, this paper proposes a deep residual convolutional neural network based bearing fault diagnosis in which multi-sensor data are converted and combined into grayscale images for feature extraction and diagnosis. The proposed approach is verified using the data of tapered roller bearings, which are tested under different rotating speeds and loads. Experimental results and comparisons show that the proposed approach can achieve promising diagnosis accuracy with high efficiency.

精确的轴承故障诊断对于工业旋转系统的安全性和可靠性至关重要。为了全面监测轴承的健康状况，通常会在轴承的不同位置安装多个传感器。不同种类或不同位置的传感器在故障诊断和定位方面具有不同的灵敏度，因此从多源数据中提取特征对故障诊断至关重要。基于深度学习的方法已被广泛应用于轴承故障诊断，并取得了显著成效。深度残差卷积神经网络作为卷积神经网络的一种改进结构，在处理深层网络时具备强大的学习能力。为提高训练效率、诊断准确率，并更好地利用轴承振动数据，本文提出一种基于深度残差卷积神经网络的轴承故障诊断方法，将多传感器数据转换并组合成灰度图像，用于特征提取与诊断。所提出的方案通过锥形滚子轴承的数据进行了验证，该轴承在不同的转速和载荷条件下进行了测试。实验结果和对比表明，该方法能够以高效率实现令人满意的诊断精度。
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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