Axial piston pump mechanical vibration transmission path and the rear shell sensitivity analysis

Considering that the distributings of sensors will affect the fault diagnosis results directly, how to distribute vibration sensors for fault diagnosis in axial piston pump are researched. The corresponding feature frequency bands of various faults are analyzed, effects of collecting signals are compared in differrent fixings of vibration sensors, placements scheme of the sensors is proposed. Combined with the experimental data, the best distributings of vibration sensors is obtained. Research result provide a strong support in the pump monitoring and fault diagnosis field and reference in other complex mechanical vibration fault diagnosis field.

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Sensitivity analysis of fluid vibration system for high speed and high pressure axial piston pump

CSAA/IET International Conference on Aircraft Utility Systems (AUS 2018) ◽

10.1049/cp.2018.0114 ◽

2018 ◽

Author(s):

Guo Changhong ◽

Cui Chao ◽

Quan Lingxiao ◽

Zhang Qiwei ◽

Jiang Hongyan

Keyword(s):

Sensitivity Analysis ◽

High Pressure ◽

High Speed ◽

Piston Pump ◽

Vibration System ◽

Axial Piston Pump ◽

Axial Piston

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Noise source identification and transmission path optimisation for noise reduction of an axial piston pump

Applied Acoustics ◽

10.1016/j.apacoust.2017.10.009 ◽

2018 ◽

Vol 130 ◽

pp. 283-292 ◽

Cited By ~ 16

Author(s):

Yang Pan ◽

Yibo Li ◽

Minghui Huang ◽

Yashi Liao ◽

Dedong Liang

Keyword(s):

Noise Reduction ◽

Source Identification ◽

Noise Source ◽

Piston Pump ◽

Axial Piston Pump ◽

Transmission Path ◽

Noise Source Identification ◽

Axial Piston

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A Parameter Sensitivity Analysis for the Dynamic Model of a Variable Displacement Axial Piston Pump

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1243/pime_proc_1987_201_115_02 ◽

1987 ◽

Vol 201 (4) ◽

pp. 235-243 ◽

Cited By ~ 26

Author(s):

S D Kim ◽

H S Cho ◽

C O Lee

Keyword(s):

Sensitivity Analysis ◽

Dynamic Model ◽

Parameter Sensitivity ◽

Piston Pump ◽

Axial Piston Pump ◽

Parameter Sensitivity Analysis ◽

System Parameters ◽

Non Linear ◽

Variable Displacement ◽

Axial Piston

Sensitivity analysis is applied to an investigation of the influence of parameters on the dynamics of a variable displacement axial piston pump. Since an exact mathematical model of piston pump is complex and highly non-linear, the investigation of its dynamic characteristics for the variation of the system parameters by changing separate parameter values in sequence becomes very ineffective. In this paper a parameter sensitivity analysis was employed to analyse the effects of the system parameters systematically. The analysis was done on the exact non-linear dynamic model of the pump system which is represented by fourth-order dynamics. Based upon the simulation results, the degree of influence of each parameter and the justification of order reduction are discussed in some detail.

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Compressed sensing reconstruction for axial piston pump bearing vibration signals based on adaptive sparse dictionary model

Measurement and Control ◽

10.1177/0020294019898725 ◽

2020 ◽

Vol 53 (3-4) ◽

pp. 649-661

Author(s):

Xiao Chaoang ◽

Tang Hesheng ◽

Ren Yan

Keyword(s):

Compressed Sensing ◽

Mechanical Vibration ◽

Sampling Theorem ◽

Piston Pump ◽

Mechanical Equipment ◽

Axial Piston Pump ◽

Vibration Signals ◽

Reconstructed Signal ◽

Bearing Vibration ◽

Axial Piston

Aiming at the mechanical equipment in the fault diagnosis process, the traditional Shannon–Nyquist sampling theorem is used for data collection, which faces main problems of storage, transmission, and processing of mechanical vibration signals. This paper presents a novel method of compressed sensing reconstruction for axial piston pump bearing vibration signals based on the adaptive sparse dictionary model. First, vibration signals were divided into blocks, and an energy sequence was produced in accordance with the energy of each signal block. Second, the energy sequence of each signal block was classified by the quantum particle swarm optimization algorithm. Finally, the reconstruction of machinery vibration signals was carried out using the K-SVD dictionary algorithm. The average relative error of the reconstructed signal obtained by the proposed algorithm is 4.25%, and the reconstruction time decreases by 43.6% when the compression ratio is 1.6.

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