Multi-Scale Attention Conditional Domain Adaptation for Electric Control Valve Fault Diagnosis Under Variable Working Conditions.

Electric control valves (ECVs) are core control components in process industries such as petrochemicals and power generation, and their operational reliability directly affects system safety and energy efficiency. However, frequent changes in the working conditions of ECVs cause vibration signals to exhibit strong nonlinearity and non-stationarity, which leads to the loss of high-frequency transient features, difficulty in extracting weak faults, and cross-condition domain shifts. These issues severely limit the generalization ability of existing fault diagnosis methods. To address this, this study proposes a collaborative fault diagnosis framework that combines a miniaturized high-frequency data acquisition system with a multi-scale attention-conditioned domain adversarial network (MS-ACDAN). First, a miniaturized high-speed data acquisition system is developed based on a field-programmable gate array (FPGA) to enable lossless acquisition of high-frequency transient signals. Subsequently, the original vibration signals are decomposed, filtered, and reconstructed using Adaptive Noise-Complete Empirical Mode Decomposition (CEEMDAN) and the Comprehensive Sensitivity Index (CSI) to generate feature-enhanced signals with high signal-to-noise ratios. Next, a feature extractor combining a one-dimensional convolutional neural network (1D-CNN) with a channel attention mechanism is constructed to automatically focus on key fault frequency band features while suppressing redundant information. Finally, a Conditional Adversarial Network (CDAN) is introduced for transfer learning. By establishing a conditional dependency between class prediction and feature representation. This approach achieves domain alignment while preserving the discriminative features of the data, thereby overcoming the limitation of traditional domain adaptation methods that ignore category information. The experimental results show that the proposed fault diagnosis framework demonstrates high recognition performance in various transfer tasks. Furthermore, even under extreme industrial noise conditions of 0 dB, the framework exhibits good diagnostic robustness. This research provides a theoretical basis and technical solution for addressing the fault diagnosis of critical control equipment under complex and variable working conditions.