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. 2023 Aug 25;23(17):7432.
doi: 10.3390/s23177432.

A Novel Cross-Sensor Transfer Diagnosis Method with Local Attention Mechanism: Applied in a Reciprocating Pump

Chen Wang  1 , Ling Chen  1 , Yongfa Zhang  1 , Liming Zhang  1   2   3 , Tian Tan  1
Affiliations

A Novel Cross-Sensor Transfer Diagnosis Method with Local Attention Mechanism: Applied in a Reciprocating Pump

Chen Wang et al. Sensors (Basel). .

Abstract

Data-driven mechanical fault diagnosis has been successfully developed in recent years, and the task of training and testing data from the same distribution has been well-solved. However, for some large machines with complex mechanical structures, such as reciprocating pumps, it is often not possible to obtain data from specific sensor locations. When the sensor position is changed, the distribution of the features of the signal data also changes and the fault diagnosis problem becomes more complicated. In this paper, a cross-sensor transfer diagnosis method is proposed, which utilizes the sharing of information collected by sensors between different locations of the machine to complete a more accurate and comprehensive fault diagnosis. To enhance the model's perception ability towards the critical part of the fault signal, the local attention mechanism is embedded into the proposed method. Finally, the proposed method is validated by applying it to experimentally acquired vibration signal data of reciprocating pumps. Excellent performance is demonstrated in terms of fault diagnosis accuracy and sensor generalization capability. The transferability of practical industrial faults among different sensors is confirmed.

Keywords: fault diagnosis; local attention mechanism; reciprocating pump; transfer learning.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Intelligent fault diagnosis by feature-based transfer learning: (a) without transfer learning, and (b) with transfer learning.
Figure 2
Figure 2
Architecture of the proposed model.
Figure 3
Figure 3
Flow chart of local attention.
Figure 4
Figure 4
Reciprocating pump’s experimental setup.
Figure 5
Figure 5
Types of reciprocating pump faults: (a) Valve Seat Compression Injury, (b) Valve Seat Erosion, (c) Valve Seat Depression, (d) Guiding Failure of Check Valve, (e) Corrosion of Valve Assembly.
Figure 6
Figure 6
Data splitting for source and target Domain.
Figure 7
Figure 7
t-SNE visualization of B→E in Task 1: (a) CNN, (b) SENet, (c) ECANet, (d) GANet, (e) proposed method.
Figure 8
Figure 8
Confusion matrix of B→E in Task 1: (a) CNN, (b) SENet, (c) ECANet, (d) GANet, (e) proposed method.
See this image and copyright information in PMC

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