Abstract
Condition monitoring has been an active area of research in many industrial fields during the last decades, particularly in fluid power systems. This paper presents a solution for the fault diagnosis of a variable displacement axial-piston pump, which is a critical component in many hydraulic systems. The proposed methodology follows a data-driven approach including data acquisition and feature extraction and is based on the analysis of acceleration signals through the theory of cyclostationarity. An experimental campaign was carried out on a laboratory test bench with the pump in the flawless state and in faulty states. Different operating conditions were considered and each test was repeated several times in order to acquire a suitable population to verify data repeatability. Results showed the capability of the proposed approach of detecting a typical fault related to worn slippers. Future works will include tests in order to apply the approach to a wider set of faults and the development of a classifier for accurate fault identification.
Highlights
In the last decades, the online condition monitoring of systems and components have been acquiring more and more importance for industries in order to definitively quit time-based maintenance and turn to condition-based maintenance
This paper presents a methodology for the analysis of acceleration signals and demonstrates its application for the diagnostics of a variable displacement axial-piston pump
A methodology based on the theory of cyclostationarity is proposed for the analysis of acceleration signals issued by roto-dynamic machines
Summary
The online condition monitoring of systems and components have been acquiring more and more importance for industries in order to definitively quit time-based maintenance and turn to condition-based maintenance. This paper presents a methodology for the analysis of acceleration signals and demonstrates its application for the diagnostics of a variable displacement axial-piston pump. This methodology relies on the theory of cyclostationarity. Following the theory of cyclostationarity, the proposed methodology decomposes the acceleration signal in its periodic first-order cyclostationary (CS1) and second-order cyclostationary (CS2) components These two parts contain information about different phenomena and need to be separated and processed with different tools. The results show that the proposed methodology for the analysis of the acceleration signals can extract features which highlight the presence of the considered fault
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