A Vibration Signal-Based Method for Fault Identification and Classification in Hydraulic Axial Piston Pumps
Paolo Casoli,
Mirko Pastori,
Fabio Scolari and
Massimo Rundo
Additional contact information
Paolo Casoli: Department of Engineering and Architecture, University of Parma, 43121 Parma, Italy
Mirko Pastori: Department of Engineering and Architecture, University of Parma, 43121 Parma, Italy
Fabio Scolari: Department of Engineering and Architecture, University of Parma, 43121 Parma, Italy
Massimo Rundo: Department of Energy, Politecnico di Torino, C.so Duca degli Abruzzi 24, 10129 Turin, Italy
Energies, 2019, vol. 12, issue 5, 1-18
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.
Keywords: fault detection; hydraulic pumps; vibration; condition monitoring (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
https://www.mdpi.com/1996-1073/12/5/953/pdf (application/pdf)
https://www.mdpi.com/1996-1073/12/5/953/ (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:12:y:2019:i:5:p:953-:d:213241
Access Statistics for this article
Energies is currently edited by Ms. Agatha Cao
More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().