A Vibration Signal-Based Method for Fault Identification and Classification in Hydraulic Axial Piston Pumps

Abstract

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.

Hybrid Intelligent Predictive Maintenance Model for Multiclass Fault Classification

“…The search displayed almost 100 research works, all focusing on various engineering setups utilising hydraulic components (Gomes et al 2016;Xu et al 2017a;Hao et al 2020;Pugin 2020). Aside from the general maintenance of the hydraulic system, other research works focus on specific key components such as the valve (Vianna et al 2015;Karanović et al 2019;Lei et al 2019), pump (Wang et al 2016;Xu et al 2017b;Casoli et al 2019), accumulator (Niu et al 2016;Pfeffer et al 2016;Leon-Quiroga et al 2020), cooler (Hathaway et al 2018).…”

Fault Diagnosis of Axial Piston Pump Based on Extreme‐Point Symmetric Mode Decomposition and Random Forests

“…In terms of extraction of fault features, Wang et al applied the minimum entropy deconvolution, Walsh transform denoising, Teager energy operator (TEO) demodulation, and other methods to the fault diagnosis of axial piston pump, which effectively solved the technical problem of extracting fault features from vibration signals contaminated by strong background noise [11][12][13]. Casoli et al presented a feature extraction using principal components analysis (PCA) techniques and different classification to detect the health state of a variable displacement axial-piston pump based on vibration signals [14]. Li et al proposed a fault feature selection method for axial piston pump based on quantum genetic algorithm [15].…”

Lumped parameter model of vane pumps developed in OpenModelica environment

“…Cavitation and aeration affect both the machine efficiency and the noise emissions, as well as the duration of the component. This issue is very challenging to be simulated but advanced CFD analysis combined with lumped parameters approach and experimental vibrational analysis are able to locate the areas where these phenomena occur and to test any design solution introduced to avoid or limit the damage ( [14,15,16]). No surprises, hence, that modelling and simulation have become essential when addressing the design of PDMs to improve the performances under different aspects.…”