2023
DOI: 10.1080/00393630.2023.2183808
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Acoustic Emission Monitoring as a Non-invasive Tool to Assist the Conservator in the Reactivation and Maintenance of Historical Vehicle Engines

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Cited by 1 publication
(11 citation statements)
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“…In this paper, we investigate the use of data analysis techniques for the visualization and classification of the engine health status using data-driven solutions such as supervised machine learning. The paper focuses on the analysis of vibro-acoustic signals for the characterization of engines in different conditions (data presented in previous publications [17][18][19][20][21]). Research questions studied in this article focus on the use of AE signals and machine learning, and can be summarized as: 1) identify the engine that produces the signal; 2) determine if an engine has an anomaly; 3) determine which engine a record belongs to and if it has an anomaly.…”
Section: Goal and Research Questionsmentioning
confidence: 99%
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“…In this paper, we investigate the use of data analysis techniques for the visualization and classification of the engine health status using data-driven solutions such as supervised machine learning. The paper focuses on the analysis of vibro-acoustic signals for the characterization of engines in different conditions (data presented in previous publications [17][18][19][20][21]). Research questions studied in this article focus on the use of AE signals and machine learning, and can be summarized as: 1) identify the engine that produces the signal; 2) determine if an engine has an anomaly; 3) determine which engine a record belongs to and if it has an anomaly.…”
Section: Goal and Research Questionsmentioning
confidence: 99%
“…The developed methodology and the results of the ACUME_HV project have been previously published in different journals and conference proceedings [17][18][19][20][21]. The ACUME_HV project showed that acoustic emission can be used to establish the state of preservation of historical engines before their reactivation and to detect the most common faults [17][18][19][20][21].…”
Section: Introductionmentioning
confidence: 99%
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