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A high-pressure hydraulically adjusted servomotor is an electromechanical–hydraulic integrated system centered on a servo valve that plays a crucial role in ensuring the safe and stable operation of steam turbines. To address the issues of difficult fault diagnoses and the low maintenance efficiency of adjusted hydraulic servomotors, this study proposes a model for detecting abnormalities of hydraulically adjusted servomotors. This model uses a multi-scale one-dimensional residual neural network (M1D_ResNet) for feature extraction and a genetic algorithm (GA)-optimized support vector data description (SVDD). Firstly, the multi-scale features of the vibration signals of the hydraulically adjusted servomotor were extracted and fused using one-dimensional convolutional blocks with three different scales to construct a multi-scale one-dimensional residual neural network binary classification model capable of recognizing normal and abnormal states. Then, this model was used as a feature extractor to create a feature set of normal data. Finally, an abnormal detection model for the hydraulically adjusted servomotor was constructed by optimizing the support vector data domain based on this feature set using a genetic algorithm. The proposed method was experimentally validated on a hydraulically adjusted servomotor dataset. The results showed that, compared with the traditional single-scale one-dimensional residual neural network, the multi-scale feature vectors fused by the multi-scale one-dimensional convolutional neural network contained richer state-sensitive information, effectively improving the performance of detecting abnormalities in the hydraulically adjusted servomotor. [ABSTRACT FROM AUTHOR]

Because of the difficulty in fault detection for and diagnosing the adjustment hydraulic servomotor, this paper uses feature extraction technology to extract the time domain and frequency domain features of the pressure signal of the adjustment hydraulic servomotor and splice the features of multiple pressure signals through the Multi-source Information Fusion (MSIF) method. The comprehensive expression of device status information is obtained. After that, this paper proposes a fault detection Algorithm GA-SVDD-neg, which uses Genetic Algorithm (GA) to optimize Support Vector Data Description with negative examples (SVDD-neg). Through joint optimization with the Mutual Information (MI) feature selection algorithm, the features that are most sensitive to the state deterioration of the adjustment hydraulic servomotor are selected. Experiments show that the MI algorithm has a better performance than other feature dimensionality reduction algorithms in the field of the abnormal detection of adjustment hydraulic servomotors, and the GA-SVDD-neg algorithm has a stronger robustness and generality than other anomaly detection algorithms. In addition, to make full use of the advantages of deep learning in automatic feature extraction and classification, this paper realizes the fault diagnosis of the adjustment hydraulic servomotor based on 1D Convolutional Neural Network (1DCNN). The experimental results show that this algorithm has the same superior performance as the traditional algorithm in feature extraction and can accurately diagnose the known faults of the adjustment hydraulic servomotor. This research is of great significance for the intelligent transformation of adjustment hydraulic servomotors and can also provide a reference for the fault warning and diagnosis of the Electro-Hydraulic (EH) system of the same type of steam turbine. [ABSTRACT FROM AUTHOR]