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A hybrid strategy of OTPA and machine learning for efficient root-cause analysis of NVH in multi-source systems

Sharif Khakshournia, Shaygan Shahed Haghighi, Marzie Majidi, Farhad Najafnia, Hamed Haddad Khodaparast Orcid Logo

Applied Acoustics, Volume: 242, Start page: 111060

Swansea University Author: Hamed Haddad Khodaparast Orcid Logo

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DOI (Published version): 10.1016/j.apacoust.2025.111060

Abstract

The growing awareness of health benefits, along with the competitive emphasis on vehicle comfort, has led automakers to place greater attention on reducing Noise, Vibration, and Harshness (NVH). One of the most beneficial techniques for NVH engineers to identify, rank, and eliminate dominant noise a...

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Published in: Applied Acoustics
ISSN: 0003-682X 1872-910X
Published: Elsevier BV 2026
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URI: https://cronfa.swan.ac.uk/Record/cronfa70543
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spelling 2025-09-30T14:09:39.6194757 v2 70543 2025-09-30 A hybrid strategy of OTPA and machine learning for efficient root-cause analysis of NVH in multi-source systems f207b17edda9c4c3ea074cbb7555efc1 0000-0002-3721-4980 Hamed Haddad Khodaparast Hamed Haddad Khodaparast true false 2025-09-30 ACEM The growing awareness of health benefits, along with the competitive emphasis on vehicle comfort, has led automakers to place greater attention on reducing Noise, Vibration, and Harshness (NVH). One of the most beneficial techniques for NVH engineers to identify, rank, and eliminate dominant noise and vibration sources and paths is Transfer Path Analysis (TPA). Unlike traditional TPA, Operational Transfer Path Analysis (OTPA) requires neither the preliminary acquisition of the transfer matrix between excitation and response points nor the measurement of forces transferred through the active and passive side connection points. Although the OTPA method offers significant advantages over classical TPA methods, it still faces challenges such as data loss caused by the pseudo-inversion of the indicator matrix. In this paper, we estimate the transmissibility matrix using a machine learning-based regression algorithm (random forest). We demonstrated that machine learning is an effective alternative to the truncated Singular Value Decomposition (SVD) method for estimating the transmissibility matrix, as it is a swift solution that preserves essential information in the indicator matrix. The efficiency of the method has been verified by a 2.28 % improvement in the Sound Pressure Level (SPL) of the driver’s ear noise of a sedan-type vehicle through the modification of the most critical path found by this approach. Journal Article Applied Acoustics 242 111060 Elsevier BV 0003-682X 1872-910X Operational transfer path analysis; Random forest; NVH diagnosis; Sensitivity analysis; Machine learning; Contribution analysis 15 1 2026 2026-01-15 10.1016/j.apacoust.2025.111060 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University SU Library paid the OA fee (TA Institutional Deal) Swansea University 2025-09-30T14:09:39.6194757 2025-09-30T13:50:24.5527755 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering Sharif Khakshournia 1 Shaygan Shahed Haghighi 2 Marzie Majidi 3 Farhad Najafnia 4 Hamed Haddad Khodaparast 0000-0002-3721-4980 5 70543__35210__12f5ae93f08d439ea3cac0795c57e54d.pdf 70543.VOR.pdf 2025-09-30T13:57:56.6410764 Output 12029445 application/pdf Version of Record true © 2025 The Authors. This is an open access article distributed under the terms of the Creative Commons CC-BY license. true eng http://creativecommons.org/licenses/by/4.0/
title A hybrid strategy of OTPA and machine learning for efficient root-cause analysis of NVH in multi-source systems
spellingShingle A hybrid strategy of OTPA and machine learning for efficient root-cause analysis of NVH in multi-source systems
Hamed Haddad Khodaparast
title_short A hybrid strategy of OTPA and machine learning for efficient root-cause analysis of NVH in multi-source systems
title_full A hybrid strategy of OTPA and machine learning for efficient root-cause analysis of NVH in multi-source systems
title_fullStr A hybrid strategy of OTPA and machine learning for efficient root-cause analysis of NVH in multi-source systems
title_full_unstemmed A hybrid strategy of OTPA and machine learning for efficient root-cause analysis of NVH in multi-source systems
title_sort A hybrid strategy of OTPA and machine learning for efficient root-cause analysis of NVH in multi-source systems
author_id_str_mv f207b17edda9c4c3ea074cbb7555efc1
author_id_fullname_str_mv f207b17edda9c4c3ea074cbb7555efc1_***_Hamed Haddad Khodaparast
author Hamed Haddad Khodaparast
author2 Sharif Khakshournia
Shaygan Shahed Haghighi
Marzie Majidi
Farhad Najafnia
Hamed Haddad Khodaparast
format Journal article
container_title Applied Acoustics
container_volume 242
container_start_page 111060
publishDate 2026
institution Swansea University
issn 0003-682X
1872-910X
doi_str_mv 10.1016/j.apacoust.2025.111060
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 - Aerospace Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering
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description The growing awareness of health benefits, along with the competitive emphasis on vehicle comfort, has led automakers to place greater attention on reducing Noise, Vibration, and Harshness (NVH). One of the most beneficial techniques for NVH engineers to identify, rank, and eliminate dominant noise and vibration sources and paths is Transfer Path Analysis (TPA). Unlike traditional TPA, Operational Transfer Path Analysis (OTPA) requires neither the preliminary acquisition of the transfer matrix between excitation and response points nor the measurement of forces transferred through the active and passive side connection points. Although the OTPA method offers significant advantages over classical TPA methods, it still faces challenges such as data loss caused by the pseudo-inversion of the indicator matrix. In this paper, we estimate the transmissibility matrix using a machine learning-based regression algorithm (random forest). We demonstrated that machine learning is an effective alternative to the truncated Singular Value Decomposition (SVD) method for estimating the transmissibility matrix, as it is a swift solution that preserves essential information in the indicator matrix. The efficiency of the method has been verified by a 2.28 % improvement in the Sound Pressure Level (SPL) of the driver’s ear noise of a sedan-type vehicle through the modification of the most critical path found by this approach.
published_date 2026-01-15T05:26:50Z
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