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.

Hydraulic pumps are the core components that provide power for hydraulic transmission systems, which are widely used in aerospace, marine engineering, and mechanical engineering, and their failure affects the normal operation of the entire system. This paper takes a single axial piston pump as the research object and proposes a small-sample fault diagnosis method based on the model migration strategy for the situation in which only a small number of training samples are available for axial piston pump fault diagnosis. To achieve end-to-end fault diagnosis, a 1D Squeeze-and-Excitation Networks (1D-SENets) model was constructed based on a one-dimensional convolutional neural network and combined with the channel domain attention mechanism. The model was first pre-trained with sufficient labeled fault data from the source conditions, and then, based on the model migration strategy, some of the underlying network parameters were fixed, and a small amount of labeled fault data from the target conditions was used to fine-tune the rest of the parameters of the pre-trained model. In this paper, the proposed method was validated using an axial piston pump fault dataset, and the experimental results show that the method can effectively improve the overfitting problem in the small sample fault diagnosis of axial piston pumps and improve the recognition accuracy.

Aiming at fault diagnosis of axial piston pumps, a new fusion method based on the extreme‐point symmetric mode decomposition method (ESMD) and random forests (RFs) was proposed. Firstly, the vibration signal of the axial piston pump was decomposed by ESMD to get several intrinsic mode functions (IMFs) and an adaptive global mean curve (AGMC) on the local side. Secondly, the total energy was selected as the data of feature extraction by analyzing the whole oscillation intensity of the signal. Thirdly, the data were preprocessed and the labels were set, and then, they were adopted as the training and testing set of machine learning samples. Lastly, the RFs model was created based on machine learning service (MLS) to diagnose the faults of the axial piston pump on the cloud. Using the test and verifying the data set for comparative testing, the fault diagnosis precision rates of the model are above 90.6%, the recall rates are more than 90.9%, the F1 score is higher than 90.7%, and the accuracy rate of this model reached 97.14%. A benchmark data simulation of mechanical transmission systems and an experimental data investigation of an axial piston pump are performed to manifest the superiority of the present method by comparing with classification and regression trees (CART) and support vector machine (SVM).

The sound signals of hydraulic pumps contain abundant key information reflecting their internal mechanical states. In environments characterized by high temperatures or high-speed rotation, or where sensor deployment is challenging, acoustic sensors offer non-contact and flexible arrangement features. Therefore, this study aims to develop an intelligent fault diagnosis method for hydraulic pumps based on acoustic signals. Initially, the Adaptive Chirp Mode Decomposition (ACMD) method is employed to remove environmental noise from the acoustic signals, enhancing the feature signals. Subsequently, the Mel spectrum is extracted as the acoustic fingerprint features of various fault states of the hydraulic pump, and these features are used to train the MobileViT network, achieving accurate identification of the different fault modes. The results indicate that the proposed Mel-MobileViT model effectively identifies and classifies various faults in constant pressure variable pumps, outperforming other models. This study not only provides an efficient and reliable intelligent method for the fault diagnosis of critical industrial equipment such as hydraulic pumps, but also offers new perspectives on the application of deep learning in acoustic pattern analysis.

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.