Research on Hydraulic System Fault Diagnosis Method Based on Machine Learning DOI

Q. Liu,

Ming Li

Engineering Research Express, Journal Year: 2024, Volume and Issue: 7(1), P. 015401 - 015401

Published: Dec. 20, 2024

Abstract This study proposes a machine learning-based fault diagnosis method for hydraulic systems, focusing on using K-means clustering algorithm data preprocessing, use Support Vector Machine (SVM) and Convolutional Neural Network Gated Recurrent Unit (CNN-GRU) models respectively classification. By performing cluster analysis sensor data, the dimensions can be effectively reduced efficiency of improved. The results show that accuracy SVM in cooler status valve classification tasks reached 99.77% 100.00% respectively. After introduction algorithm, its training time was significantly reduced, showing extremely high real-time capabilities. CNN-GRU model performs particularly well handling complex tasks, especially accumulator task, with an rate as 96.60%, which is better than model. Although longer, advantages pattern recognition give it obvious high-accuracy application scenarios. Multi-fault conducted, achieves best performance without employing clustering, emphasizing importance preserving integrity original

Language: Английский

Marine diesel engine piston ring fault diagnosis based on LSTM and improved beluga whale optimization DOI Creative Commons

Bingwu Gao,

Jing Xu, Huajin Zhang

et al.

Alexandria Engineering Journal, Journal Year: 2024, Volume and Issue: 109, P. 213 - 228

Published: Sept. 5, 2024

Language: Английский

Citations

4
Investigation on the flow characteristics of a centrifugal pump with a fractured leading edge of a single blade DOI
Huairui Li, Rongyong Zhang, Qiang Fu

et al.

Physics of Fluids, Journal Year: 2025, Volume and Issue: 37(1)

Published: Jan. 1, 2025

Centrifugal pumps are essential in various industrial applications, and their stable efficient operation has a direct impact on the overall performance of system. This study simulates different lengths fractures at LE (leading edge) single blade to conduct an in-depth analysis effects internal flow transient characteristics. The reveals that most significant pump occur near rate 0.8Qd, where head efficiency can decrease by up 6.19% 3.77%, respectively, compared original blades. Blade lead deterioration pressure suction sides, creating vortices inducing leakage flow, while entropy production significantly increases this area. A 230.1% increase distribution angle 26.6% maximum radial force reflect changes distribution. Also, make wall pulsations stronger SPF (shaft passing frequency), they amplitude surface much bigger both 3SPF frequencies. Finally, change way vibrations behave measurement points x y directions big way. acceleration amplitudes frequencies go 125.8%, 193.1%, 62.5%, 184.6%, respectively. These findings provide important theoretical basis for early warning diagnosis fracture failures.

Language: Английский

Citations

0
Theoretical investigation of convex representation based interpretable time-frequency weight optimization for Machine health monitoring DOI
Tongtong Yan, Dong Wang, Tangbin Xia

et al.

Mechanical Systems and Signal Processing, Journal Year: 2025, Volume and Issue: 230, P. 112625 - 112625

Published: March 30, 2025

Language: Английский

Citations

0
A Data-Softmax-LightGBM based fault diagnosis approach for metal additive manufacturing circulation filtration systems DOI
Xiaojuan Peng, Lingfeng Wang, Yehai Li

et al.

Measurement, Journal Year: 2025, Volume and Issue: unknown, P. 117900 - 117900

Published: May 1, 2025

Language: Английский

Citations

0
DSTF-Net: A Novel Framework for Intelligent Diagnosis of Insulated Bearings in Wind Turbines with Multi-Source Data and Its Interpretability DOI
Tongguang Yang, Ming Xu,

Chun‐Lung Chen

et al.

Renewable Energy, Journal Year: 2024, Volume and Issue: unknown, P. 121965 - 121965

Published: Nov. 1, 2024

Language: Английский

Citations

2
A hybrid intelligent diagnostic approach for spool jamming faults of hydraulic directional valves DOI
Weidong Li,

Heping Jiang,

Chunhua Feng

et al.

Measurement, Journal Year: 2024, Volume and Issue: 241, P. 115706 - 115706

Published: Sept. 10, 2024

Language: Английский

Citations

2
A Novel Intelligent Condition Monitoring Framework of Essential Service Water Pumps DOI Creative Commons
Yingqian Liu, Qian Huang, Huairui Li

et al.

Applied System Innovation, Journal Year: 2024, Volume and Issue: 7(4), P. 61 - 61

Published: July 19, 2024

Essential service water pumps are necessary safety devices responsible for discharging waste heat from containments through seawater; their condition monitoring is critical the safe and stable operation of seaside nuclear power plants. However, it difficult to directly apply existing intelligent methods these pumps. Therefore, an framework designed, including parallel implementation unsupervised anomaly detection fault diagnosis. A model preselection algorithm based on highest validation accuracy proposed diagnosis selection among models. novel information integration fuse output According experimental results modules, a kernel principal component analysis using mean fusion processing multi-channel data (AKPCA (fusion)) selected, support vector machine (SVM selected. The overall test false negative rate AKPCA (fusion) 0.83 0.144, respectively, f1-score SVM 0.966 1, respectively. (fusion), show that successfully avoids lack abnormal status misdiagnosis. meaningful attempt achieve complex equipment.

Language: Английский

Citations

1
Intelligent condition monitoring for the vertical centrifugal pump using multimodal signals and hybrid models DOI
Qiang Fu, Yingqian Liu, Rongyong Zhang

et al.

Measurement, Journal Year: 2024, Volume and Issue: unknown, P. 115813 - 115813

Published: Sept. 1, 2024

Language: Английский

Citations

1
Dual-feature enhanced hybrid convolutional network for imbalanced fault diagnosis of rolling bearings DOI Creative Commons
Yingjie Zhao, Changfeng Yan, Bin Liu

et al.

Measurement Science and Technology, Journal Year: 2024, Volume and Issue: 36(1), P. 016023 - 016023

Published: Oct. 30, 2024

Abstract Deep learning has been extensively applied in the rolling bearing fault diagnosis domain due to its superior data analysis and feature extraction capabilities. However, practical applications, normal operating state occupies most of service life equipment, occurrence probability each kind is different, leading imbalanced that significantly degrades performance neural network. In order solve this problem, a dual-feature enhanced hybrid convolutional network (DEHCNet) proposed. Firstly, an impulse segment enhancement module constructed enhance features raw data, helping learn more accurately. Then, designed fully mine discriminant minority classes from data. addition, feature-enhanced combinational pooling devised guide focus on critical maximize retention key dimensionality reduction operations, thereby reducing influence imbalance classifier. Finally, three distinct datasets are used verify DEHCNet. Experimental results show better diagnostic accuracy robustness under conditions imbalance.

Language: Английский

Citations

0
The Intelligent Diagnosis of a Hydraulic Plunger Pump Based on the MIGLCC-DLSTM Method Using Sound Signals DOI Creative Commons
L. L. Ma, Anqi Jiang, Wanlu Jiang

et al.

Machines, Journal Year: 2024, Volume and Issue: 12(12), P. 869 - 869

Published: Nov. 29, 2024

To fully exploit the rich state and fault information embedded in acoustic signals of a hydraulic plunger pump, this paper proposes an intelligent diagnostic method based on sound signal analysis. First, were collected under normal various conditions. Then, four distinct features—Mel Frequency Cepstral Coefficients (MFCCs), Inverse Mel (IMFCCs), Gammatone (GFCCs), Linear Prediction (LPCCs)—were extracted integrated into novel hybrid cepstral feature called MIGLCCs. This fusion enhances model’s ability to distinguish both high- low-frequency characteristics, resist noise interference, capture resonance peaks, achieving complementary advantage. Finally, MIGLCC set was input double layer long short-term memory (DLSTM) network enable recognition pump’s operational states. The results indicate that MIGLCC-DLSTM achieved accuracy 99.41% test Validation CWRU bearing dataset data from high-pressure servo motor turbine system yielded overall accuracies 99.64% 98.07%, respectively, demonstrating robustness broad application potential method.

Language: Английский

Citations

0