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oai:uvadoc.uva.es:10324/752222025-03-04T20:01:19Zcom_10324_43510com_10324_954com_10324_894col_10324_43513

Data augmentation in predictive maintenance applicable to hydrogen combustion engines: a review Schwarz, Alexander Rodríguez Rahal, Jhonny Sahelices Fernández, Benjamín Barroso García, Verónica Weis, Ronny Duque Antón, Simón Data augmentation Predictive maintenance Anomaly detection Generative models 33 Ciencias Tecnológicas Producción Científica Machine-learning-based predictive maintenance models, i.e. models that predict break- downs of machines based on condition information, have a high potential to minimize maintenance costs in industrial applications by determining the best possible time to per- form maintenance. Modern machines have sensors that can collect all relevant data of the operating condition and for legacy machines which are still widely used in the industry, retrofit sensors are readily, easily and inexpensively available. With the help of this data it is possible to train such a predictive maintenance model. The main problem is that most data is obtained from normal operating conditions, whereas only limited data are from failures. This leads to highly unbalanced data sets, which makes it very difficult, if not impossible, to train a predictive maintenance model that can detect faults reliably and timely. Another issue is the lack of available real data due to privacy concerns. To address these problems, a suitable data generation strategy is needed. In this work, a litera- ture review is conducted to identify a solution approach for a suitable data augmentation strategy that can be applied to our specific use case of hydrogen combustion engines in the automotive field. This literature review shows that, among the different state-of-the-art proposals, the most promising for the generation of reliable synthetic data are the ones based on generative models. The analysis of the different metrics used in the state of the art allows to identify the most suitable ones to evaluate the quality of generated signals. Finally, an open problem in research in this area is identified and it is the need to validate the plausibility of the data generated. The generation of results in this area will contribute decisively to the development of predictive maintenance models. Publicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCLE This work was supported by the German Federal Ministry for Economic Affairs and Climate Action (BMWK), under grant No. (19I21028R) Ministerio de Ciencia, Innovación y Universidades MICIU - projects CPP2022-009735 and PID2020-115468RB-I00 Her research was also funded by the “CIBER-Cons- orcio Centro de Investigación Biomédica en Red” (CB19/01/00012) through “Instituto de Salud Carlos III”, co-funded with European Regional Development Fund. 2025-03-04T12:47:49Z 2025-03-04T12:47:49Z 2024 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Artificial Intelligence Review, 2024, vol. 58, n.1 https://uvadoc.uva.es/handle/10324/75222 10.1007/s10462-024-11021-9 1 Artificial Intelligence Review 58 1573-7462 eng https://link.springer.com/article/10.1007/s10462-024-11021-9 Atribución 4.0 Internacional info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ © 2024 The Author(s) application/pdf Springer https://uvadoc.uva.es/bitstream/10324/75222/4/Data-augmentation-predictive.pdf.jpg Hispana TEXT http://creativecommons.org/licenses/by/4.0/ UVaDOC. Repositorio Documental de la Universidad de Valladolid https://uvadoc.uva.es/handle/10324/75222