| 谢佳琪,尤 伟,沈长青,朱忠奎.基于改进卷积深度置信网络的轴承故障诊断研究[J].电子测量与仪器学报,2020,34(2):36-43 |
| 基于改进卷积深度置信网络的轴承故障诊断研究 |
| Bearing fault diagnosis based on improved convolution deep belief network |
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| DOI： |
| 中文关键词: 故障诊断 轴承 特征学习 卷积深度置信网络 |
| 英文关键词:mechanical fault diagnosis bearing feature learning convolution deep belief network |
| 基金项目:国家自然科学基金项目面上项目（51875376，51875375）资助 |
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| 中文摘要: |
| 机械设备故障诊断在工业应用中具有重要的意义。传统的基于振动信号处理与分析的故障诊断方法，依赖于丰富的专业知识和人工经验，难以保证准确的特征提取与故障诊断。利用深度学习方法可以自动学习数据深层次特征的特点，提出一种基于改进卷积深度置信网络的滚动轴承故障定性、定量诊断方法。首先，为了提供较好的浅层输入，将原始振动信号转换至频域信号；其次，在模型训练过程中，引入Adam优化器，加快模型训练，提高模型收敛速度;最后，为了充分发挥模型各层特征表征能力,对模型结构进行优化，提出多层特征融合学习结构，以提高模型的泛化能力。实验结果表明，所提出的改进模型相比于传统的栈式自动编码器、人工神经网络、深度置信网络以及标准卷积深度信念网络，具有更好的诊断精度，有效地实现了轴承故障的定性、定量化诊断。 |
| 英文摘要: |
| Mechanical equipment fault detection is of great significance in industrial applications. The traditional fault diagnosis method based on vibration signal processing and analysis relies on rich professional knowledge and artificial experience, and it is difficult to achieve accurate feature extraction and fault diagnosis. In this paper, the deep learning method can be used to automatically learn the characteristics of deep features from the data. A qualitative and quantitative diagnosis method for rolling bearing faults based on improved convolution deep belief network is proposed. First, in order to provide better shallow inputs, the original vibration signal is converted to the frequency domain signal by the fast Fourier transform. Secondly, in the process of model training, the Adam optimizer is introduced to speed up the model training and improve the convergence speed of the model. Finally, in order to make full use of the characterization capabilities of each layer, the model structure is optimized to come up with a multi layer feature and fusion learning structure is proposed to improve the generalization ability of the model. The experimental results show that the proposed improved model has better diagnostic accuracy than the traditional stack autoencoder (SAE), artificial neural network (ANN), deep belief network (DBN) and standard convolution deep belief network (CDBN). It has better diagnostic accuracy and effectively realizes qualitative and quantitative diagnosis of bearing faults. |
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