Data analysis techniques for the visualization and classification of historical vehicle engines’ health status using data-driven solutions

Stefano Carrino; Luca Meyer; Jonathan Dreyer; Brice Chalançon; Alejandro Roda-Buch; Laura Brambilla

doi:10.14568/cp30818

Autores

Stefano Carrino HES-SO // University of Applied Sciences and Arts Western Switzerland, Haute Ecole Arc Ingénierie, Espace de l’Europe 11, 2000 Neuchatel, Switzerland https://orcid.org/0000-0001-5171-6541
Luca Meyer HES-SO // University of Applied Sciences and Arts Western Switzerland, Haute Ecole Arc Ingénierie, Espace de l’Europe 11, 2000 Neuchatel, Switzerland
Jonathan Dreyer HES-SO // University of Applied Sciences and Arts Western Switzerland, Haute Ecole Arc Ingénierie, Espace de l’Europe 11, 2000 Neuchatel, Switzerland
Brice Chalançon Association de Gestion du Musée National de l’Automobile, 188 Av. de Colmar, 68100 Mulhouse, France
Alejandro Roda-Buch HES-SO // University of Applied Sciences and Arts Western Switzerland, Haute Ecole Arc Conservation-restauration, Espace de l’Europe 11, 2000 Neuchatel, Switzerland; Ecole Polytechnique Fédérale de Lausanne, EPFL CH-1015, Lausanne, Switzerland https://orcid.org/0000-0002-4036-1017
Laura Brambilla Haute Ecole Arc Conservation-resturation - HES-SO University of Applied Sciences and Arts of Western Switzerland https://orcid.org/0000-0002-7197-6524

DOI:

https://doi.org/10.14568/cp30818

Palavras-chave:

Aprendizagem automática, Património cultural, Monitorização não invasiva, Emissão acústica

Resumo

Em património cultural a utilização de técnicas não destrutivas para determinar o estado de
conservação de um artefacto é de extrema importância para evitar danos no próprio objeto. Neste artigo, apresentamos um canal de dados e técnicas de aprendizagem automática para a visualização, análise e caraterização de motores de veículos históricos. O artigo investiga a utilização de sinais vibro-acústicos adquiridos nos motores em diferentes estados de conservação e condições de
funcionamento para treinar soluções de aprendizagem automática. Os dados são classificados de acordo com o seu estado de conservação e a presença de anomalias. O algoritmo t-SNE utilizou-se para a redução da dimensionalidade para a visualização dos dados. Os algoritmos de aprendizagem automática testados revelaram um desempenho promissor na associação dos dados de emissões acústicas com a assinatura do motor, o tipo de anomalia e as suas condições de funcionamento.
Porém, um maior conjunto de dados permitir-nos-ia melhorar e reforçar os resultados.

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