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 Bearing Fault Diagnosis Method Based on Branch Convolution Neural Network
基于分支卷积神经网络的轴承故障诊断方法 |
| 作 者： |
Jie Hu
Hairong Yan |
| 关键词： |
Intelligent fault diagnosis [智能故障诊断]
Deep learning [ 深度学习]
Class imbalance [ 类别不平衡]
Convolution neural network [ 卷积神经网络] |
| 出版年： |
2023 |
| 文集名： |
2nd IEEE International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA 2023)
第二届IEEE国际电气工程、大数据与算法会议（EEBDA 2023） |
| 会议名： |
2nd IEEE International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA 2023)
第二届IEEE国际电气工程、大数据与算法会议（EEBDA 2023） |
| 届 次： |
2nd |
| 摘 要： |
Rolling bearings are one of the most important components of rotating machinery, to ensure stability, reliability, and safety of its operation, intelligent fault diagnosis plays a vital role. In the industry, the condition of class imbalance and few fault samples exists widely in the fault diagnosis field. In order to diagnose the state of bearings accurately under the kind of conditions, this paper proposes a one-dimensional Branch Convolutional Neural Network with Wide first-layer kernel (BWCNN) to reduce the impact of data imbalance and few fault samples. First, the normalization was applied to the original time-domain data to attenuate the effect of distribution differences of different features on the model and improve the convergence speed of the model. Then, using a two-branch network constructed by a one-dimensional convolutional network with a first-layer wide convolutional kernel, the model parameters was iteratively updated during the training phase of the network with resampling strategy and a cumulative learning strategy, making it possible for the model to learn generic patterns for all classes of samples and fault patterns for a few classes. Finally, the trained model was used for fault diagnosis. The proposed method's performance was evaluated using two widely used experimental datasets. On the selected datasets, the average macro F1 score reaches 99.5% when the healthy samples on the training set divided by the sum of all fault samples on the training set equals 20, and the number of samples for each class of faults on the training set is 10.
滚动轴承是旋转机械中最重要的部件之一，为确保其运行的稳定性、可靠性和安全性，智能故障诊断发挥着关键作用。在工业领域，故障诊断中普遍存在类别不平衡和故障样本数量少的问题。为了在这些条件下准确诊断轴承状态，本文提出了一种一维分支卷积神经网络（BWCNN），该网络采用宽第一层卷积核，以降低数据不平衡和少量故障样本带来的影响。首先，对原始时域数据进行归一化处理，以减弱不同特征分布差异对模型的影响，并加快模型收敛速度。接着，通过构建由一维卷积网络与宽第一层卷积核组成的双分支网络，在训练阶段采用重采样策略和累积学习策略对模型参数进行迭代更新，使模型能够学习到所有类别的通用模式，以及少数类别的特定故障模式。最后，训练好的模型被用于故障诊断。通过两个广泛使用的实验数据集对所提出方法的性能进行了评估。在选定的数据集上，当训练集中健康样本数量与训练集中所有故障样本总数之比为20，且训练集中每类故障的样本数量均为10时，平均宏F1分数达到99.5%。 |
| 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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