In recent years, the interest of industry towards condition-based maintenance, substituting traditional time-based maintenance, is growing. Indeed, condition-based maintenance can increase the system uptime with a consequent economic advantage. In this paper, a solution to detect the health state of a variable displacement axial-piston pump based on vibration signals is proposed. The pump was tested on the test bench in different operating points, both in healthy and faulty conditions, the latter obtained by assembling damaged components in the pump. The vibration signals were acquired and exploited to extract features for fault identification. After the extraction, the obtained features were reduced to decrease the computational effort and used to train different types of classifiers. The classification algorithm that presents the greater accuracy with reduced features was identified. The analysis has also showed that using the time sampling raw signal, a satisfying accuracy could be obtained, which will permit onboard implementation. Results have shown the capability of the algorithm to identify which fault occurred in the system (fault identification) for each working condition. In future works, the classification algorithm will be implemented onboard to validate its effectiveness for the online identification of the typical incipient faults in axial-piston pumps.
Pressure ripple has always been a major drawback in hydraulic circuits, since it is the main source of the overall noise emitted by the pump. This article presents a theoretical analysis on the active control of the pressure ripple in an axial piston pump by properly moving the swash plate. The reduction of the pressure oscillations is studied in an active way and for this purpose a mathematical model of the whole pump has been developed, focusing on both the fluid dynamic aspects and the component dynamics. An experimental activity has been performed in order to validate the pump mathematical model. The results of the simulation, compared with the experimental data, highlight a suitable capability of the model to predict both the dynamics of the swash plate and the delivery pressure ripple. The validated model has been used for implementing an active control of the pressure ripple with the aim of properly modifying the machine displacement at high frequency in order to vary the instantaneous delivery flow rate and, consequently, the outlet pressure. The control strategy is grounded on moving the swash plate for modifying the motion law of the pistons through a servo valve integrated into the displacement control system. The simulations results have demonstrated that acting on the pump displacement control is possible to considerably reduce the amplitude of the pressure oscillations.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.