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. 2024 Nov 22;26(12):1007.
doi: 10.3390/e26121007.

A Real-Time Fault Diagnosis Method for Multi-Source Heterogeneous Information Fusion Based on Two-Level Transfer Learning

Danmin Chen  1   2 , Zhiqiang Zhang  3 , Funa Zhou  3 , Chaoge Wang  3
Affiliations

A Real-Time Fault Diagnosis Method for Multi-Source Heterogeneous Information Fusion Based on Two-Level Transfer Learning

Danmin Chen et al. Entropy (Basel). .

Abstract

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.

Keywords: information fusion; real-time fault diagnosis; transfer learning.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Two-dimensional screenshot diagram.
Figure 2
Figure 2
Fault diagnosis framework of multi-source heterogeneous information fusion based on two-level transfer learning.
Figure 3
Figure 3
Flowchart of the multi-source heterogeneous information fusion fault diagnosis method based on two-level transfer learning.
Figure 4
Figure 4
Schematic diagram of the gearbox test platform.
Figure 5
Figure 5
Screenshot image of photoelectric speed sensor monitoring: (a) normal; (b) pitting; (c) tooth breakage; (d) broken wear; (e) point wear.
Figure 6
Figure 6
Bar chart of online diagnosis time for various models on the gearbox dataset.
Figure 7
Figure 7
Bar chart of the fault diagnosis accuracy for various models on the gearbox dataset.
Figure 8
Figure 8
Screenshot image of fan end monitoring: (a) normal; (b) inner ring fault; (c) ball bearing fault; (d) outer ring fault.
Figure 9
Figure 9
Bar chart of the online diagnosis time for various models on the bearing dataset.
Figure 10
Figure 10
Bar chart of fault diagnosis accuracy for various models on the bearing dataset.
See this image and copyright information in PMC

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