Key Takeaways & Executive Findings
- •• Proposes a deep transfer learning method for electromagnetic pulse valve fault diagnosis under small sample conditions, using simulated data to augment real data. • Identifies an optimal parameter transfer strategy that fine-tunes both feature extractor and classifier, combined with an attention mechanism to enhance multi-sensor feature integration. • Achieves high transfer accuracies of 93.5% and 94.2% with only 7.9% and 8.2% of the total dataset as small sample data, respectively. • Addresses the practical challenge of scarce fault data in industrial settings, offering a cost-effective and reliable diagnostic solution.
Abstract
The electromagnetic pulse valve, as a key component in baghouse dust removal systems, plays a crucial role in the performance of the system. However, despite the promising results of intelligent fault diagnosis methods based on extensive data in diagnosing electromagnetic valves, real-world diagnostic scenarios still face numerous challenges. Collecting fault data for electromagnetic pulse valves is not only time-consuming but also costly, making it difficult to obtain sufficient fault data in advance, which poses challenges for small sample fault diagnosis. To address this issue, this paper proposes a fault diagnosis method for electromagnetic pulse valves based on deep transfer learning and simulated data. This method achieves effective transfer from simulated data to real data through four parameter transfer strategies, which combine parameter freezing and fine-tuning operations. Furthermore, this paper identifies a parameter transfer strategy that simultaneously fine-tunes the feature extractor and classifier, and introduces an attention mechanism to integrate fault features, thereby enhancing the correlation and information complementarity among multi-sensor data. The effectiveness of the proposed method is evaluated through two fault diagnosis cases under different operating conditions. In this study, small sample data accounted for 7.9% and 8.2% of the total dataset, and the experimental results showed transfer accuracies of 93.5% and 94.2%, respectively, validating the reliability and effectiveness of the method under small sample conditions.
1. Introduction
Bag filters play an important role in controlling air pollution. An electromagnetic pulse valve serves as a key component in pulse bag filters for dust removal. Its proper functionality directly affects the efficacy of dust removal and the continuous operation of the pulse bag filter. Hence, timely and accurate diagnosis of the electromagnetic pulse valve’s operational status is imperative to minimize economic losses resulting from system failures [1].
Deep learning (DL), characterized by its deep structure, is adept at learning deep feature representations and nonlinear mapping relationships [2, 3]. It possesses a strong ability to extract discriminant features from a large amount of raw data. In recent years, many DL networks have been studied for valve fault-diagnosis methods [4–8]. Typically, intelligent diagnosis models based on deep networks rely on thorough analysis of machine monitoring data [9]. The more sufficient training data, the richer the fault types in the training set, and the higher the diagnostic accuracy of the intelligent diagnosis model. However, it is usually difficult to obtain a large amount of historical fault data from the equipment to be diagnosed in advance, with only limited fault data available under specific working conditions [10–12]. The main reasons are as follows: (1) In engineering scenarios, machines primarily operate under normal conditions, with failures occurring infrequently. Consequently, the majority of collected data pertains to normal operation, making it arduous to gather sufficient failure data directly from engineering environments to support the training of intelligent diagnosis models [13, 14]. (2) The cost associated with conducting fault simulation experiments is high. For example, to generate fault data for an electromagnetic pulse valve in a laboratory setting, researchers must deliberately destroy the spring and diaphragm to induce faults. In addition, establishing a fault simulation test platform for data acquisition is essential. For a 1.5-inch pulse valve operating with a pulse signal duration of 100 ms, the gas consumption during operation ranges from 250–450 L. Such experiments not only incur significant costs but also demand substantial manpower, making it difficult to collect sufficient fault data for fault simulation experiments. (3) The fault data obtained from computer simulations cannot be used directly. Dynamic modeling inevitably involves simplifying the equipment, focusing solely on key components, and overlooking intricate details of the real system. Moreover, uncertainties and random factors stemming from the environment remain unresolved in the modelin
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Tao Wang, Min Wang, Bo Wang, Lianghao Ma (2025). Deep Transfer Learning Based Fault Diagnosis for Electromagnetic Pulse Valve Faults Under Small Sample. Chinese Journal of Mechanical Engineering. https://doi.org/10.1186/s10033-025-01341-4
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Frequently Asked Questions
What is the main challenge addressed in this paper?
The main challenge is the difficulty in obtaining sufficient fault data for electromagnetic pulse valves in real-world scenarios, which is time-consuming and costly, making small sample fault diagnosis a significant issue.
How does the proposed method handle small sample fault diagnosis?
The method uses deep transfer learning and simulated data, employing four parameter transfer strategies that combine parameter freezing and fine-tuning to transfer knowledge from simulated to real data effectively.
What is the role of the attention mechanism in the proposed method?
The attention mechanism is introduced to integrate fault features from multi-sensor data, enhancing the correlation and information complementarity among the data, which improves diagnostic accuracy.
What were the experimental results in terms of transfer accuracy?
In two fault diagnosis cases under different operating conditions, the method achieved transfer accuracies of 93.5% and 94.2%, using small sample data that accounted for only 7.9% and 8.2% of the total dataset, respectively.
Why is fault data collection for electromagnetic pulse valves difficult?
Fault data collection is difficult because machines usually operate under normal conditions, failures are infrequent, fault simulation experiments are costly and require deliberate damage to components, and computer simulations cannot directly provide usable data due to simplifications and unresolved environmental uncertainties.
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