Quantitative Fault Diagnostics of Hydraulic Cylinder Using Particle Filter

Condition-based hydraulic cylinder maintenance necessitates quantitative fault diagnostics. However, existing methods are characterized by either qualitative or limited quantitative capabilities. In this paper, a quantitative fault diagnostic method using a particle filter for hydraulic cylinders is...

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Bibliographic Details
Published in:Machines (Basel) 2023-11, Vol.11 (11), p.1019
Main Authors: Zhang, Yakun, Vacca, Andrea, Gong, Guofang, Yang, Huayong
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
condition-based maintenance
Cylinders
Decision making
Dynamic models
Fault diagnosis
fault diagnostics
Fourier transforms
hydraulic cylinder
Hydraulics
Leakage
Machine learning
Mathematical models
Methods
Neural networks
Nonlinear dynamics
Parameter estimation
particle filter
Physics
Qualitative analysis
state estimation
Trends
Wavelet transforms
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Summary:Condition-based hydraulic cylinder maintenance necessitates quantitative fault diagnostics. However, existing methods are characterized by either qualitative or limited quantitative capabilities. In this paper, a quantitative fault diagnostic method using a particle filter for hydraulic cylinders is proposed. The problem of quantitative fault diagnostics is formally formulated in a stochastic framework to assess the health/fault state, and an architecture based on joint state-parameter estimation is proposed. Through the establishment and analysis of a nonlinear dynamic model of the hydraulic cylinder, the impact of time-varying parameters on the state variables is revealed. Three fault modes of the cylinder, including friction, internal leakage, and external leakage, are theoretically identified. The proposed method allows for a simultaneous quantitative diagnosis of these three fault modes. The performance of the proposed method is evaluated using meticulously designed experiments. The results demonstrate that the mean absolute percentage errors in the parameter estimations are below 9% (accuracy exceeding 91%), thus validating its feasibility and effectiveness.
ISSN:2075-1702
2075-1702
DOI:10.3390/machines11111019