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A fault prognosis strategy for an external gear pump using Machine Learning algorithms and synthetic data generation methods

Kayal Lakshmanan, Fabrizio Tessicini, Antonio Gil Orcid Logo, Ferdinando Auricchio Orcid Logo

Applied Mathematical Modelling, Volume: 123, Pages: 348 - 372

Swansea University Authors: Kayal Lakshmanan, Antonio Gil Orcid Logo

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DOI (Published version): 10.1016/j.apm.2023.07.001

Abstract

Fault prognosis is an important area of research that aims to predict and diagnose faults in complex systems. The sudden failure of industrial components can have adverse consequences for an organisation in terms of time, cost and workflow. It is, therefore, critical to ensure the maintenance of equ...

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Published in: Applied Mathematical Modelling
ISSN: 0307-904X
Published: Elsevier BV 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa63774
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The sudden failure of industrial components can have adverse consequences for an organisation in terms of time, cost and workflow. It is, therefore, critical to ensure the maintenance of equipment components in optimal condition in order to avoid downtime that may cause significant disruption. Due to this reason, in recent years, there has been an increasing interest in creating innovative methods for fault prognosis that can increase system reliability and reduce maintenance costs. Gear pumps are widely utilised in a variety of industrial applications, and their reliability and effectiveness are crucial for achieving optimal system performance. Gear pumps, on the other hand, are prone to malfunctions and failures, which can result in substantial downtime and maintenance costs. The challenge is to develop a fault prognosis approach that is reliable and accurate enough to detect and diagnose defects in a gear pump before they cause system failures. This paper proposes a novel computational strategy for the fault prognosis of an external gear pump using Machine Learning (ML) approaches. Due to the unavailability of sufficient experimental datain the vicinity of failure mechanisms, a novel approach to generating a high-fidelity in-silico dataset via a Computational Fluid Dynamic (CFD) model of the gear pump in healthy and faulty working conditions is presented. However, considering the computational demand for rerunning the same CFD simulations, novel synthetic data generation techniques are implemented by perturbing the frequency content of the time series to recreate other working conditions and constructing degradation behaviour using linear and cubice interpolation methods for run-to-failure scenarios. The synthetically created datasets are used to train the underlying ML metamodel for fault prognosis. Two types of ML algorithms are employed for fault prognosis: Multilayer Perceptron (MLP) and Support Vector Machine (SVM) algorithms. A series of numerical examples are shown, allowing us to infer that the proposed modelling technique is feasible in an industrial setting and that employing the MLP algorithm delivers superior fault prognosis results when compared to SVM. Furthermore, datasets generated using the cubic interpolation method have lower prediction errorsthan datasets generated using the linear interpolation method due to the smoother degradation behaviour in the data.</abstract><type>Journal Article</type><journal>Applied Mathematical Modelling</journal><volume>123</volume><journalNumber/><paginationStart>348</paginationStart><paginationEnd>372</paginationEnd><publisher>Elsevier BV</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>0307-904X</issnPrint><issnElectronic/><keywords>Fault prognosis, Machine learning, High-fidelity model, Synthetic data generation</keywords><publishedDay>30</publishedDay><publishedMonth>11</publishedMonth><publishedYear>2023</publishedYear><publishedDate>2023-11-30</publishedDate><doi>10.1016/j.apm.2023.07.001</doi><url>http://dx.doi.org/10.1016/j.apm.2023.07.001</url><notes/><college>COLLEGE NANME</college><department>Mechanical Engineering</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>MECH</DepartmentCode><institution>Swansea University</institution><apcterm>SU Library paid the OA fee (TA Institutional Deal)</apcterm><funders>Swansea University. 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spelling v2 63774 2023-07-04 A fault prognosis strategy for an external gear pump using Machine Learning algorithms and synthetic data generation methods 31fdeba4e76994bc72c5b8954389f8ab Kayal Lakshmanan Kayal Lakshmanan true false 1f5666865d1c6de9469f8b7d0d6d30e2 0000-0001-7753-1414 Antonio Gil Antonio Gil true false 2023-07-04 MECH Fault prognosis is an important area of research that aims to predict and diagnose faults in complex systems. The sudden failure of industrial components can have adverse consequences for an organisation in terms of time, cost and workflow. It is, therefore, critical to ensure the maintenance of equipment components in optimal condition in order to avoid downtime that may cause significant disruption. Due to this reason, in recent years, there has been an increasing interest in creating innovative methods for fault prognosis that can increase system reliability and reduce maintenance costs. Gear pumps are widely utilised in a variety of industrial applications, and their reliability and effectiveness are crucial for achieving optimal system performance. Gear pumps, on the other hand, are prone to malfunctions and failures, which can result in substantial downtime and maintenance costs. The challenge is to develop a fault prognosis approach that is reliable and accurate enough to detect and diagnose defects in a gear pump before they cause system failures. This paper proposes a novel computational strategy for the fault prognosis of an external gear pump using Machine Learning (ML) approaches. Due to the unavailability of sufficient experimental datain the vicinity of failure mechanisms, a novel approach to generating a high-fidelity in-silico dataset via a Computational Fluid Dynamic (CFD) model of the gear pump in healthy and faulty working conditions is presented. However, considering the computational demand for rerunning the same CFD simulations, novel synthetic data generation techniques are implemented by perturbing the frequency content of the time series to recreate other working conditions and constructing degradation behaviour using linear and cubice interpolation methods for run-to-failure scenarios. The synthetically created datasets are used to train the underlying ML metamodel for fault prognosis. Two types of ML algorithms are employed for fault prognosis: Multilayer Perceptron (MLP) and Support Vector Machine (SVM) algorithms. A series of numerical examples are shown, allowing us to infer that the proposed modelling technique is feasible in an industrial setting and that employing the MLP algorithm delivers superior fault prognosis results when compared to SVM. Furthermore, datasets generated using the cubic interpolation method have lower prediction errorsthan datasets generated using the linear interpolation method due to the smoother degradation behaviour in the data. Journal Article Applied Mathematical Modelling 123 348 372 Elsevier BV 0307-904X Fault prognosis, Machine learning, High-fidelity model, Synthetic data generation 30 11 2023 2023-11-30 10.1016/j.apm.2023.07.001 http://dx.doi.org/10.1016/j.apm.2023.07.001 COLLEGE NANME Mechanical Engineering COLLEGE CODE MECH Swansea University SU Library paid the OA fee (TA Institutional Deal) Swansea University. The authors would also like to acknowledge the financial support received through the European Commission EACEA Agency, Framework Partnership Agreement 2013-0043 Erasmus Mundus Action 1b, as a part of the EM Joint Doctorate “Simulation in Engineering and Entrepreneurship Development (SEED)”. 2023-09-05T11:50:59.7317913 2023-07-04T11:23:52.9905833 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering Kayal Lakshmanan 1 Fabrizio Tessicini 2 Antonio Gil 0000-0001-7753-1414 3 Ferdinando Auricchio 0000-0002-3735-2400 4 63774__28201__0875298f1e7d40caae71adfec9ab9b45.pdf 63774.pdf 2023-07-27T13:06:26.0714233 Output 4241624 application/pdf Version of Record true © 2023 The Author(s). Published by Elsevier Inc. Distributed under the terms of a Creative Commons Attribution 4.0 License (CC BY 4.0). true eng https://creativecommons.org/licenses/by/4.0/
title A fault prognosis strategy for an external gear pump using Machine Learning algorithms and synthetic data generation methods
spellingShingle A fault prognosis strategy for an external gear pump using Machine Learning algorithms and synthetic data generation methods
Kayal Lakshmanan
Antonio Gil
title_short A fault prognosis strategy for an external gear pump using Machine Learning algorithms and synthetic data generation methods
title_full A fault prognosis strategy for an external gear pump using Machine Learning algorithms and synthetic data generation methods
title_fullStr A fault prognosis strategy for an external gear pump using Machine Learning algorithms and synthetic data generation methods
title_full_unstemmed A fault prognosis strategy for an external gear pump using Machine Learning algorithms and synthetic data generation methods
title_sort A fault prognosis strategy for an external gear pump using Machine Learning algorithms and synthetic data generation methods
author_id_str_mv 31fdeba4e76994bc72c5b8954389f8ab
1f5666865d1c6de9469f8b7d0d6d30e2
author_id_fullname_str_mv 31fdeba4e76994bc72c5b8954389f8ab_***_Kayal Lakshmanan
1f5666865d1c6de9469f8b7d0d6d30e2_***_Antonio Gil
author Kayal Lakshmanan
Antonio Gil
author2 Kayal Lakshmanan
Fabrizio Tessicini
Antonio Gil
Ferdinando Auricchio
format Journal article
container_title Applied Mathematical Modelling
container_volume 123
container_start_page 348
publishDate 2023
institution Swansea University
issn 0307-904X
doi_str_mv 10.1016/j.apm.2023.07.001
publisher Elsevier BV
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering
url http://dx.doi.org/10.1016/j.apm.2023.07.001
document_store_str 1
active_str 0
description Fault prognosis is an important area of research that aims to predict and diagnose faults in complex systems. The sudden failure of industrial components can have adverse consequences for an organisation in terms of time, cost and workflow. It is, therefore, critical to ensure the maintenance of equipment components in optimal condition in order to avoid downtime that may cause significant disruption. Due to this reason, in recent years, there has been an increasing interest in creating innovative methods for fault prognosis that can increase system reliability and reduce maintenance costs. Gear pumps are widely utilised in a variety of industrial applications, and their reliability and effectiveness are crucial for achieving optimal system performance. Gear pumps, on the other hand, are prone to malfunctions and failures, which can result in substantial downtime and maintenance costs. The challenge is to develop a fault prognosis approach that is reliable and accurate enough to detect and diagnose defects in a gear pump before they cause system failures. This paper proposes a novel computational strategy for the fault prognosis of an external gear pump using Machine Learning (ML) approaches. Due to the unavailability of sufficient experimental datain the vicinity of failure mechanisms, a novel approach to generating a high-fidelity in-silico dataset via a Computational Fluid Dynamic (CFD) model of the gear pump in healthy and faulty working conditions is presented. However, considering the computational demand for rerunning the same CFD simulations, novel synthetic data generation techniques are implemented by perturbing the frequency content of the time series to recreate other working conditions and constructing degradation behaviour using linear and cubice interpolation methods for run-to-failure scenarios. The synthetically created datasets are used to train the underlying ML metamodel for fault prognosis. Two types of ML algorithms are employed for fault prognosis: Multilayer Perceptron (MLP) and Support Vector Machine (SVM) algorithms. A series of numerical examples are shown, allowing us to infer that the proposed modelling technique is feasible in an industrial setting and that employing the MLP algorithm delivers superior fault prognosis results when compared to SVM. Furthermore, datasets generated using the cubic interpolation method have lower prediction errorsthan datasets generated using the linear interpolation method due to the smoother degradation behaviour in the data.
published_date 2023-11-30T11:51:01Z
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score 11.035655

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