Chen Danmin, Zhang Zhiqiang, Zhou Funa, Wang Chaoge
School of Computer and Artificial Intelligence, Henan Finance University, Zhengzhou 450046, China.
Zhengzhou Key Laboratory of Financial Big Data Intelligent Application Technology, Zhengzhou 450046, China.
Entropy (Basel). 2024 Nov 22;26(12):1007. doi: 10.3390/e26121007.
A convolutional neural network can extract features from high-dimensional data, but the convolution operation has a high time complexity and requires a large amount of computation. For equipment with a high sampling frequency, fault diagnosis methods based on convolutional neural networks cannot meet the requirements of online fault diagnosis. To solve this problem, this study proposes a fault diagnosis method for multi-source heterogeneous information fusion based on two-level transfer learning. This method aims to fully utilize multi-source heterogeneous information and external domain data, construct a two-level transfer mechanism to fuse multi-source heterogeneous information, avoid convolutional operations, and achieve real-time fault diagnosis. Its main work is to build a feature extraction network model of screenshots, design a mechanism for transfer from the feature extraction model using screenshots to the deep learning model using one-dimensional sequence signals, and complete the transfer from a convolutional neural network to a deep neural network. After two-level transfer, the fault diagnosis model not only integrates the characteristics of one-dimensional sequence signals and screenshots but also avoids convolution operations and has a low time complexity. The effectiveness of the proposed method is verified using a gearbox dataset and a bearing dataset.
卷积神经网络可以从高维数据中提取特征，但卷积操作具有较高的时间复杂度，需要大量的计算。对于采样频率较高的设备，基于卷积神经网络的故障诊断方法无法满足在线故障诊断的要求。为了解决这个问题，本研究提出了一种基于两级迁移学习的多源异构信息融合故障诊断方法。该方法旨在充分利用多源异构信息和外部领域数据，构建两级迁移机制来融合多源异构信息，避免卷积操作，并实现实时故障诊断。其主要工作是构建截图的特征提取网络模型，设计一种从使用截图的特征提取模型到使用一维序列信号的深度学习模型的迁移机制，并完成从卷积神经网络到深度神经网络的迁移。经过两级迁移后，故障诊断模型不仅融合了一维序列信号和截图的特征，还避免了卷积操作，且时间复杂度较低。使用齿轮箱数据集和轴承数据集验证了所提方法的有效性。
