Técnicas de análise de dados para a visualização e classificação do estado de conservação de motores de veículos históricos
DOI:
https://doi.org/10.14568/cp30818Palavras-chave:
Aprendizagem automática, Património cultural, Monitorização não invasiva, Emissão acústicaResumo
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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Referências
Ashton, J.; Hallam, D., ‘The conservation of functional objects – An ethical dilemma’, AICCM Bulletin 16(3) (1990) 19-26, https://doi.org/10.1179/bac.1990.16.3.003.
Lemos, M.; Tissot, I., ‘Reflections on the conservation challenges of scientific and technological objects’, Conservar Património 33 (2020) 24-31, https://doi.org/10.14568/cp2018044.
Wain, A., ‘The importance of movement and operation as preventive conservation strategies for heritage machinery’, Journal of the American Institute for Conservation 56(2) (2017) 81-95, https://doi.org/10.1080/01971360.2017.1326238.
Newey, H., ‘Conservation and the preservation of scientific and industrial collections’, Studuies in Conservation 45(sup1) (2000) 137-139, https://doi.org/10.1179/sic.2000.45.Supplement-1.137.
Pye, E., ‘Challenges of conservation: working objects’, Science Museum Group Journal 6 (2016), https://dx.doi.org/10.15180/160608/001.
Scruby, C. B., ‘An introduction to acoustic emission’, Journal of Physics E Scientific Instruments 20(8) (1987) 946-953, https//doi.org/10.1088/0022-3735/20/8/001.
Delvecchio, S.; Bonfiglio, P.; Pompoli, F., ‘Vibro-acoustic condition monitoring of internal combustion engines: a critical review of existing techniques’, Mechanical Systems and Signal Processing 99 (2018) 661-683, https://doi.org/10.1016/j.ymssp.2017.06.033.
Kaul, B. C.; Lawler, B.; Zahdeh, A., ‘Engine diagnostics using acoustic emissions sensors’, SAE International Journal of Engines 9(2) (2016) 684-692, https://doi.org/10.4271/2016-01-0639.
Douglas, R. M.; Steel, J. A.; Reuben, R. L., ‘A study of the tribological behaviour of piston ring/cylinder liner interaction in diesel engines using acoustic emission’, Tribology International 39(12) (2006) 1634-1642, https://doi.org/10.1016/j.triboint.2006.01.005.
Nivesrangsan, P.; Steel, J. A.; Reuben, R. L., ‘Source location of acoustic emission in diesel engines’, Mechanical Systems and Signal Processing 21(2) (2007) 1103-1114, https://doi.org/10.1016/j.ymssp.2005.12.010.
Wu, J.-D.; Liu, C.-H., ‘An expert system for fault diagnosis in internal combustion engines using wavelet packet transform and neural network’, Expert Systems with Applications 36(3) (2009) 4278-4286, https://doi.org/10.1016/j.eswa.2008.03.008.
Johansson, S.; Nilsson, P. H.; Ohlsson, R.; Rosén, B.-G., ‘Experimental friction evaluation of cylinder liner/piston ring contact’, Wear 271(3-4) (2011) 625-633, https://doi.org/10.1016/j.wear.2010.08.028.
Wei, N.; Gu, J. X.; Gu, F.; Chen, Z.; Li, G.; Wang, T.; Ball, A. D., ‘An Investigation into the acoustic emissions of internal combustion engines with modelling and wavelet package analysis for monitoring lubrication conditions’, Energies 12(4) (2019) 640-659, https://doi.org/10.3390/en12040640.
Łukomski, M.; Bratasz, Ł; Hagan, E.; Strojecki, M.; Laudato Beltran, V., ‘Acoustic emission monitoring for cultural heritage’, Getty Conservation Institute, Los Angeles (2020).
Thickett, D.; Cheung, C. S.; Liang, H.; Twydle, J.; Maev, R. G.; Gavrilov, D., ‘Using non-invasive nondestructive techniques to monitor cultural heritage objects’, Insight - Non-destructive testing and Condition Monitoring 59(5) (2017) 230-234, https://doi.org/10.1784/insi.2017.59.5.230.
Le Conte, S.; Vaiedelich, S.; Thomas, J. H.; Muliava, V.; de Reyer, D.; Maurin, E., ‘Acoustic emission to detect xylophagous insects in wooden musical instrument’, Journal of Cultural Heritage 16(3) (2015) 338-343, https://doi.org/10.1016/j.culher.2014.07.001.
Chalançon, B., Les mesures d’émission acoustique appliquées aux moteurs d’automobiles de collection patrimoniale comme outil de diagnostic avant la remise en fonctionnement, Master dissertation, Haute Ecole Arc Conservation-restauration (HES-SO), Neuchâtel (2019) https://doc.rero.ch/record/327368 (accessed 2023-03-30).
Roda Buch, A.; Cornet, E.; Rapp, G.; Chalançon, B.; Mischler, S.; Brambilla, L., ‘Development of a diagnostic tool based on acoustic emission techniques’, in 500 ans de tribologie, Presses des Mines, Paris (2021) 147-152.
Roda Buch, A.; Cornet, E.; Rapp, G.; Chalançon, B.; Mischler, S.; Brambilla, L., ‘Fault detection and diagnosis of historical vehicle engines using acoustic emission techniques’, Acta Imeko 10(1) (2021) 77-83, https://doi.org/10.21014/acta_imeko.v10i1.853.
Brambilla, L.; Chalançon, B.; Roda Buch, A.; Cornet, E.; Rapp, G.; Mischler, S., ‘Acoustic emission techniques for the detection of simulated failures in historical vehicles engines’, The European Physical Journal Plus 136(6) (2021) 641, https://doi.org/10.1140/epjp/s13360-021-01611-9.
Chalançon, B.; Roda Buch, A.; Cornet, E.; Rapp, G.; Weisser, T.; Brambilla, L., ‘Acoustic emission monitoring as a non-invasive tool to assist the conservator in the reactivation and maintenance of historical vehicle engines’, Studies in Conservation (2023), https://doi.org/10.1080/00393630.2023.2183808.
Carrino, S.; Guerne, J.; Dreyer, J.; Ghorbel, H.; Schorderet, A.; Montavon, R., ‘Machining quality prediction using acoustic sensors and machine learning’, Proceedings 63(1) (2020) 31, https://doi.org/10.3390/proceedings2020063031.
Li, Z.; Li, J.; Wang, Y.; Wang, K., ‘A deep learning approach for anomaly detection based on SAE and LSTM in mechanical equipment’, The International Journal of Advanced Manufacturing Technology 103 (2019) 499-510, https://doi.org/10.1007/s00170-019-03557-w.
Malhotra, P.; Vig, L.; Shroff, G.; Agarwal, P., ‘Long short term memory networks for anomaly detection in time series’, in Proceedings of European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN, i6doc.com publ., Bruges (2015) 89-94, https://www.esann.org/proceedings/2015 (accessed 2023-07-03).
Kim, D.; Antariksa, G.; Handayani, M. P.; Lee, S.; Lee, J., ‘Explainable anomaly detection framework for maritime main engine sensor data’, Sensors 21(15) (2021) 5200, https://doi.org/10.3390/s21155200.
Kim, J-M.; Baik, J., ‘Anomaly detection in sensor data’, Journal of Applied Reliability 18(1) (2018) 20-32, https://koreascience.kr/article/JAKO201815565837435.pdf (accessed 2023-07-03).
Gokhale, M. Y.; Khanduja, D. K., ‘Time domain signal analysis using wavelet packet decomposition approach’, International Journal of Communications, Network and System Sciences 3(3) (2010) 321-329, https://doi.org/10.4236/ijcns.2010.33041.
Yu, Y.; Si, X.; Hu, C.; Zhang, J., ‘A review of recurrent neural networks: LSTM cells and network architectures’, Neural computation 31(7) (2019) 1235-1270, https://doi.org/10.1162/neco_a_01199.
Shensa, M. J., ‘The discrete wavelet transform: wedding the a trous and Mallat algorithms’, IEEE Transactions on Signal Processing 40(10) (1992) 2464-2482, https://doi.org/10.1109/78.157290.
Van der Maaten, L.; Hinton, G., ‘Visualizing data using t-SNE’, Journal of Machine Learning Research 9(11) (2008) 2579-2605, https://www.jmlr.org/papers/volume9/vandermaaten08a/vandermaaten08a.pdf (accessed 2023-07-05).
‘Shankarpandala - lazypredict’, in Github, https://github.com/shankarpandala/lazypredict (accessed 2023-07-03).
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