A methodology for detection of wear in hydraulic axial piston pumps
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TECHNICAL PAPER
A methodology for detection of wear in hydraulic axial piston pumps Jessica Gissella Maradey Lázaro1 · Carlos Borrás Pinilla2 Received: 27 May 2020 / Accepted: 24 July 2020 © Springer-Verlag France SAS, part of Springer Nature 2020
Abstract An effective asset management has a direct impact on maintenance costs, reliability, and equipment availability, especially in hydraulic machinery. Variable displacement axial piston pump is a major component used in the industry due to its load capacity ratio, pressure management, and high performance. Some of the main faults are wear and abrasion of the valve plates, increasing pressure losses as well as temperature, decreasing volumetric efficiency, and abnormal vibration. The off-line methodology implemented includes preprocessing of the vibration signals taken from the test bench available for this study, the feature extraction using wavelets, a stage of detection and classification through the use of artificial neural networks. Several networks were assessment, such as Adaline, nonlinear, and multilayer perceptron networks. Classification percentages greater than 90% are obtained taking into consideration 5 wear conditions related to the loss of volumetric efficiency. Keywords Axial piston pump · Fault diagnosis · Artificial neural networks
1 Introduction In the search to find the best solutions that allow effective asset management, for preventing damage to equipment and consequently its availability and reliability in industrial operations are lost. Preventive and predictive maintenance programs seem to be the most commonly used strategy to manage the operation and maintenance of hydraulics equipment. Condition-based maintenance (CBM) is conceived as an effective prognostics and health management system (PHM) that offers an economic and affordable alternative to timely diagnose equipment by determining its current status, taking action, and predicting potential failures [1]. Besides, the relevant advantage is that it makes the most of each element without sacrificing reliability, which results in maximization of profit for the company, achieving savings of up to 80% in time and costs [2].
* Jessica Gissella Maradey Lázaro [email protected] Carlos Borrás Pinilla [email protected] 1
Universidad Autónoma de Bucaramanga, Avenida 42 #48‑11 Cabecera del Llano, Bucaramanga, Santander, Colombia
Universidad Industrial de Santander, Bucaramanga, Santander, Colombia
2
A CBM program includes tasks such as data acquisition, data analysis and expert systems to make a maintenance decision and to design policies [3]. Especially, hydraulic equipment usually presents failures associated with wear and leakage of its internal components, causing a considerable decreasing in its performance, whose occurrence and severity, in some cases, are difficult to predict under conventional data analysis systems (i.e. vibration analysis, thermography, and oil analysis) due to the complex non-linear dynamic behavior, as well as of its geometry and high-performance req
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