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国外文献 > 1.轴承 > 6. 信号处理与特征分析 > 特征与分类

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标 题： A Novel Bearing Fault Diagnosis of Raw Signals Based on 1D Residual Convolution Neural Network
基于一维残差卷积神经网络的原始信号新型轴承故障诊断
作 者： Chuanjiang Li
Qiang He
Liya Yu
Wanli Yang
Ansi Zhang
Zhongjing Duan
关键词： Fault diagnosis  [故障诊断]
Raw signals  [ 原始信号]
Convolution neural network  [ 卷积神经网络]
Residual blocks  [ 残差块]
Feature extraction  [ 特征提取]
出版年： 2020
会议名： 2020 International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS 2020)
2020年高性能大数据与智能系统国际会议（HPBD&IS 2020）
届 次：
摘 要： Diagnosing the faults of rolling bearing is an important thing, because it is the key component as well as the most easily damaged part in mechanical equipment. Whereas the traditional intelligent diagnosis methods rely heavily on the complex time-consuming feature extraction processes and rich diagnostic experience. Using the characteristics of one-dimensional vibration signals, we propose a one-dimensional residual convolution neural network (1D-RCNN) that directly uses time-domain waveforms as input. By introducing 1D CNN, residual blocks, batch normalization, and global average pooling layer, our model can effectively extract and identify features through deep convolution neural networks automatically without overfitting. A series of experiments were conducted based on the dataset of Case Western Reserve University (CWRU) to evaluate the performance of our proposed approach. According to the results, the proposed method is superior to state-of-the-art approaches using handcrafted features or raw data as input, and has certain practical potential in industrial production.

诊断滚动轴承的故障具有重要意义，因为它是机械设备中的关键部件，同时也是最容易损坏的部分。而传统的智能诊断方法则依赖于复杂的耗时特征提取过程以及丰富的诊断经验。本文利用一维振动信号的特性，提出了一种直接以时域波形为输入的一维残差卷积神经网络（1D-RCNN）。通过引入一维卷积神经网络、残差块、批归一化和全局平均池化层，我们的模型能够通过深度卷积神经网络自动高效地提取并识别特征，同时避免过拟合。基于Case Western Reserve大学（CWRU）的数据集，我们开展了一系列实验来评估所提方法的性能。结果表明，该方法优于当前使用人工设计特征或原始数据作为输入的先进方法，在工业生产中具有一定的实际应用潜力。
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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