Experimental investigation on cavitation and cavitation detection of axial piston pump based on MLP-Mixer

Lan, Yuan; Li, Zhijie; Liu, Shengzheng; Huang, Jiahai; Niu, Linkai; Xiong, Xuejian; Niu, Chenguang; Wu, Bi-Ru; Zhou, Xu; Yan, Jinbao; An, Siyuan; Lv, Jishuang

doi:10.1016/j.measurement.2022.111582

Cited by 18 publications

(3 citation statements)

References 20 publications

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“…Aiming at the fault diagnosis of hydraulic systems, the fault features of the hydraulic pump, valve, and cylinder were extracted for fault diagnosis. Reference [17] used a cavitation detection framework, including experimental research and numerical signal processing, to detect the strength of cavitation faults in axial piston pumps, so as to improve the accuracy of fault diagnosis. Reference [18] improved the identification accuracy and optimized the parameters of the hydraulic plunker pump through the improved LeNet-5 and PSO hyperparameter optimization fault diagnoses.…”

Section: Introductionmentioning

confidence: 99%

A Fault Diagnosis Method of the Shearer Hydraulic Heightening System Based on a Rough Set and RBF Neural Network

2023

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The hydraulic heightening system is the core component of the shearer, and its stable operation directly affects the safety and reliability of the equipment, so it is of great significance to realize an efficient and accurate fault diagnosis. This paper proposes a fault diagnosis method combining a rough set and radial basis function neural network (RS-RBFNN). Firstly, the RS is used to discretize the original fault data set and attribute reduction, remove the redundant information, and mine the implicit knowledge and potential rules. Then, the topology structure of the RBFNN is determined. The mapping relationship is established between the fault symptom and category. The fault diagnosis is carried out with Python language. Finally, the method is compared with two diagnostic methods including a back propagation neural network (BPNN) and RBFNN. The research results show that the RS-RBFNN has the highest fault diagnosis accuracy, with an average of 98.68%, which verifies the effectiveness of the proposed fault diagnosis method.

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Section: Introductionmentioning

confidence: 99%

A Fault Diagnosis Method of the Shearer Hydraulic Heightening System Based on a Rough Set and RBF Neural Network

2023

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“…Yan [14] et al proposed a gear RUL prediction model based on LSTM (long short-term memory networks), which improved prediction accuracy and robustness by combining the tree structure with LSTM. Lan [15] et al proposed a cavitation detection model based on MLP-Mixer (multilayer perceptron), which is used to recognize the cavitation intensity of the axial piston pump with given working conditions. Zhou [16] et al proposed an improved SVM (support vector machine), which was optimized by the BAS (beetle antennae search) algorithm and PSO (particle swarm optimization) algorithm to achieve high-precision classification of ultrasonic signals.…”

Section: Introductionmentioning

confidence: 99%

A Self-Attention Integrated Learning Model for Landing Gear Performance Prediction

Lin

Tong

Guo

et al. 2023

Sensors

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The landing gear structure suffers from large loads during aircraft takeoff and landing, and an accurate prediction of landing gear performance is beneficial to ensure flight safety. Nevertheless, the landing gear performance prediction method based on machine learning has a strong reliance on the dataset, in which the feature dimension and data distribution will have a great impact on the prediction accuracy. To address these issues, a novel MCA-MLPSA is developed. First, an MCA (multiple correlation analysis) method is proposed to select key features. Second, a heterogeneous multilearner integration framework is proposed, which makes use of different base learners. Third, an MLPSA (multilayer perceptron with self-attention) model is proposed to adaptively capture the data distribution and adjust the weights of each base learner. Finally, the excellent prediction performance of the proposed MCA-MLPSA is validated by a series of experiments on the landing gear data.

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“…With the development of hydraulic technology, piston pumps are widely used in fields [1] such as engineering machinery, mining metallurgy, and agricultural machinery due to their compact structure, high power density, and convenient variable control [2,3]. The axial piston pump is a typical positive-displacement hydraulic machine that relies on the relative motion of various internal components [4,5] to complete the flow distribution action.…”

mentioning

confidence: 99%

Analysis of Slipper Overturning and Collision Behavior of the Axial Piston Pump Based on Rigid-Flexible Coupling

et al. 2023

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In the working process of the axial piston pump, the composite multi-degree of freedom motion of the slipper and its coupling, as well as the complex working conditions, cause the slipper to overturn and collide irregularly with its coupling. The overturning of the slipper leads to partial abrasion of the slipper, and the magnitude of the force generated by direct solid contact between the slipper and the swash plate significantly impacts the wear speed of the slipper. This article is based on the rigid-flexible coupling model of the axial piston pump jointly established by Adams and Ansys. It analyzes the collision relationship between the slipper and its coupling, the overturning behavior characteristics of the slipper, and the variation law in the overturning and collision behavior of the slipper under different spring forces and load pressure. The research results indicate that the overturning and collision behavior of the slipper exhibits a certain periodicity. Appropriate spring preload and piston chamber oil pressure can effectively reduce the overturning degree of the slipper, reduce the collision strength between the slipper and its coupling, and thus reduce the partial abrasion of the slipper. The research results of this article provide a reference for the wear failure analysis and structural optimization of the slipper.

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Experimental investigation on cavitation and cavitation detection of axial piston pump based on MLP-Mixer

Cited by 18 publications

References 20 publications

A Fault Diagnosis Method of the Shearer Hydraulic Heightening System Based on a Rough Set and RBF Neural Network

A Fault Diagnosis Method of the Shearer Hydraulic Heightening System Based on a Rough Set and RBF Neural Network

A Self-Attention Integrated Learning Model for Landing Gear Performance Prediction

Analysis of Slipper Overturning and Collision Behavior of the Axial Piston Pump Based on Rigid-Flexible Coupling

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