8 results on '"Yang, Xukang"'
Search Results
2. Intelligent Fault Diagnosis Method for Constant Pressure Variable Pump Based on Mel-MobileViT Lightweight Network.
- Author
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Zhao, Yonghui, Jiang, Anqi, Jiang, Wanlu, Yang, Xukang, Xia, Xudong, and Gu, Xiaoyang
- Subjects
FAULT diagnosis ,CONVOLUTIONAL neural networks ,SENSOR placement ,DEEP learning ,VOICEPRINTS ,HUMAN fingerprints - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
3. Detecting Anomalies in Hydraulically Adjusted Servomotors Based on a Multi-Scale One-Dimensional Residual Neural Network and GA-SVDD.
- Author
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Yang, Xukang, Jiang, Anqi, Jiang, Wanlu, Zhao, Yonghui, Tang, Enyu, and Qi, Zhiqian
- Subjects
CONVOLUTIONAL neural networks ,SUPPORT vector machines ,VECTOR data ,ANOMALY detection (Computer security) ,GENETIC algorithms - Abstract
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]
- Published
- 2024
- Full Text
- View/download PDF
4. Reduced Graphene Oxides Skeleton Hybrid Interface Enables Long Lifespan Zinc Metal Anodes.
- Author
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Huang, Shichuang, Zheng, Shuyang, Yang, Xukang, Zhong, Huohong, Zheng, Chengtao, and Zhang, Nan
- Subjects
CHEMICAL kinetics ,ZIRCONIUM oxide ,GRAPHENE oxide ,ELECTRICAL energy ,DENDRITIC crystals - Abstract
With the ever‐growing demands for the electrical energy storage market, aqueous zinc‐ion batteries (AZIBs) are showing great prospects owing to their good capacity, low cost, and high safety. Nevertheless, they also face significant challenges, particularly in the form of zinc dendrite growth and inadequate stability. Here, a strategy based on reduced graphene oxides‐zirconium dioxide (rGO/ZrO2) hybrid interface to enable long lifespan zinc metal anodes is proposed. Via in situ electrochemical deposition and self‐assembly method, rGO and ZrO2 are deposited on the zinc metal anode successively, and their loading is optimized subsequently. In the functionalized interface, rGO serves as the skeleton and improves electrolyte ion diffusion, while ZrO2 relieves the electric field concentration problem and inhibits dendrite growth. As a result, the Zn@rGO@ZrO2 symmetric cell achieves a highly reversible cycle of 3000 h at 2.0 mA cm−2/1.0 mA h cm−2 and fast reaction kinetics. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
5. Small-Sample Fault Diagnosis of Axial Piston Pumps across Working Conditions, Based on 1D-SENet Model Migration.
- Author
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Yang, Xukang, Jiang, Anqi, Jiang, Wanlu, Yue, Yi, Jing, Lei, and Zhou, Junjie
- Subjects
HYDRAULIC control systems ,CONVOLUTIONAL neural networks ,RECIPROCATING pumps ,FAULT diagnosis ,MARINE engineering - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
6. Fault Diagnosis of Axial Piston Pump Based on Extreme-Point Symmetric Mode Decomposition and Random Forests
- Author
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Lei Yafei, Jiang Wanlu, Niu Hongjie, Shi Xiaodong, and Yang Xukang
- Subjects
Physics ,QC1-999 - Abstract
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).
- Published
- 2021
- Full Text
- View/download PDF
7. Fault Diagnosis of Axial Piston Pump Based on Extreme-Point Symmetric Mode Decomposition and Random Forests
- Author
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Niu Hongjie, Lei Ya-fei, Yang Xukang, Jiang Wan-lu, and Shi Xiaodong
- Subjects
Piston pump ,Article Subject ,Computer science ,Physics ,QC1-999 ,020209 energy ,Mechanical Engineering ,Feature extraction ,Axial piston pump ,02 engineering and technology ,Geotechnical Engineering and Engineering Geology ,Condensed Matter Physics ,Random forest ,Support vector machine ,Data set ,020303 mechanical engineering & transports ,0203 mechanical engineering ,Mechanics of Materials ,0202 electrical engineering, electronic engineering, information engineering ,Extreme point ,F1 score ,Algorithm ,Civil and Structural Engineering - Abstract
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).
- Published
- 2021
8. RUL Prediction of Rolling Bearings Based on a DCAE and CNN
- Author
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Wang, Chenyang, primary, Jiang, Wanlu, additional, Yang, Xukang, additional, and Zhang, Shuqing, additional
- Published
- 2021
- Full Text
- View/download PDF
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