As the actuator of the turbine speed control system, the performance and response characteristics of the speed control valve actuator directly affect the operational economy, maneuverability, and reliability of the turbine unit. When faults occur in scenarios where data scarcity is coupled with high noise levels, existing deep neural network models are limited by their inability to extract key discriminative features from noisy signals and by the lack of sufficient training information. This limitation hinders the development and application of highly reliable fault diagnosis systems. We propose a novel fault diagnosis framework, Resformer, which is designed to address the challenges posed by data scarcity and high noise coupling, as well as the highly coupled and complex fault modes in electro-hydraulic systems. The Resformer framework offers a highly interpretable feature selection and fusion strategy to identify key features. It also integrates the Local Binary Pattern algorithm to extract local features from grayscale images of multi-sensor data, significantly enhancing the representativeness and noise resistance of the dataset. Moreover, to strengthen the Resformer’s multi-scale feature extraction capability and noise robustness, a multi-kernel dilated convolutional residual network architecture is introduced, enabling the discovery of critical discriminative features under conditions of data scarcity and high noise coupling. The proposed efficient multi-scale self-attention mechanism effectively extracts important features at different scales, further improving the performance of Resformer. Experiments conducted on the GVA testbed have validated the effectiveness and robustness of Resformer.

中文翻译：

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Resformer：在数据稀缺和高噪声耦合场景中对限速器阀执行器进行故障诊断的端到端框架


作为涡轮机速度控制系统的执行器，调速阀执行器的性能和响应特性直接影响涡轮机组的运行经济性、机动性和可靠性。当在数据稀缺与高噪声水平相结合的情况下发生故障时，现有的深度神经网络模型会受到限制，因为它们无法从噪声信号中提取关键判别特征，并且缺乏足够的训练信息。这种限制阻碍了高可靠性故障诊断系统的开发和应用。我们提出了一种新的故障诊断框架 Resformer，旨在解决数据稀缺和高噪声耦合带来的挑战，以及电液系统中高度耦合和复杂的故障模式。Resformer 框架提供了高度可解释的特征选择和融合策略来识别关键特征。它还集成了 Local Binary Pattern 算法，从多传感器数据的灰度图像中提取局部特征，显著增强了数据集的代表性和抗噪性。此外，为了增强 Resformer 的多尺度特征提取能力和噪声鲁棒性，引入了多核扩张卷积残差网络架构，能够在数据稀缺和高噪声耦合的情况下发现关键判别特征。所提出的高效多尺度自注意力机制有效地提取了不同尺度下的重要特征，进一步提高了 Resformer 的性能。在 GVA 测试台上进行的实验验证了 Resformer 的有效性和稳健性。