TY - JOUR
T1 - A methodology for detection of wear in hydraulic axial piston pumps
AU - Maradey Lázaro, Jessica Gissella
AU - Borrás Pinilla, Carlos
N1 - Publisher Copyright:
© 2020, Springer-Verlag France SAS, part of Springer Nature.
PY - 2020/9/1
Y1 - 2020/9/1
N2 - 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.
AB - 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.
KW - Artificial neural networks
KW - Axial piston pump
KW - Fault diagnosis
UR - http://www.scopus.com/inward/record.url?scp=85088857877&partnerID=8YFLogxK
U2 - 10.1007/s12008-020-00681-w
DO - 10.1007/s12008-020-00681-w
M3 - Artículo Científico
AN - SCOPUS:85088857877
SN - 1955-2513
VL - 14
SP - 1103
EP - 1119
JO - International Journal on Interactive Design and Manufacturing
JF - International Journal on Interactive Design and Manufacturing
IS - 3
ER -