2026-04-19T04:45:27Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/92822024-06-18T00:05:23Zcom_10259_4219com_10259_5086com_10259_2604col_10259_4220

Ramírez Sanz, José Miguel 750 600 0000-0003-0189-8046 Maestro-Prieto, Jose A. f7654090-c6c7-427a-92c0-2bd8b72e3660 600 Arnaiz González, Álvar 39 600 0000-0001-6965-0237 Bustillo Iglesias, Andrés 81 600 0000-0003-2855-7532 2024-06-17T11:01:44Z 2024-06-17T11:01:44Z 2023-12 0019-0578 http://hdl.handle.net/10259/9282 10.1016/j.isatra.2023.09.027 The automation of Fault Detection and Diagnosis (FDD) is a central task for many industries today. A myriad of methods are in use, although the most recent leading contenders are data-driven approaches and especially Machine Learning (ML) methods. ML algorithms fall into two main categories: supervised and unsupervised methods, depending on whether or not the instances are labeled with the expected outputs. However, a new approach called Semi-Supervised Learning (SSL) has recently emerged that uses a few labeled instances together with other unlabeled instances for the training process. This new approach can significantly improve the accuracy of conventional ML models for industrial environments where labeled data are scarce. SSL has been tested as a promising solution over the past few years for several FDD problems, although there have been no systemic reviews of this sort of approach up until the present review. In this study, an attempt to organize the existing literature on SSL for FDD using the taxonomy of van Engelen & Hoos is reported. The most and the least frequently used SSL algorithms are identified and considered in terms of different fault detection tasks and their most common dataset structure. Moreover, a set of best practices are proposed in the conclusions of this work for implementation under real industrial conditions, so as to avoid some of the most common faults. This work was supported by the Junta de Castilla y León under project BUR055P20 (JCyL/FEDER, UE), the Ministry of Science and Innovation of Spain under the projects PID2020-119894GB-I00 and TED2021-129485B-C43, co-financed through European Union FEDER funds. It also was supported through the Consejería de Educación of the Junta de Castilla y León and the European Social Fund through a pre-doctoral grant (EDU/875/2021). application/pdf eng Elsevier ISA Transactions. 2023, V. 143, p. 255-270 https://doi.org/10.1016/j.isatra.2023.09.027 Atribución 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Industrial fault detection and diagnosis Machine learning Semi-supervised learning Systemic review Vibrations Informática Bioinformática Computer science Bioinformatics Semi-supervised learning for industrial fault detection and diagnosis: A systemic review info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion ISA Transactions 143 255 270 THUMBNAIL Ramirez-isat_2023.pdf.jpg Ramirez-isat_2023.pdf.jpg IM Thumbnail image/jpeg 4887 https://riubu.ubu.es/bitstream/10259/9282/4/Ramirez-isat_2023.pdf.jpg f43432335b96f2adb63bb5c22d437dc3 MD5 4 LICENSE license.txt license.txt text/plain; charset=utf-8 999 https://riubu.ubu.es/bitstream/10259/9282/3/license.txt b295bcbce42e2caabeb0c623d3860c06 MD5 3 CC-LICENSE license_rdf license_rdf application/rdf+xml; charset=utf-8 908 https://riubu.ubu.es/bitstream/10259/9282/2/license_rdf 0175ea4a2d4caec4bbcc37e300941108 MD5 2 ORIGINAL Ramirez-isat_2023.pdf Ramirez-isat_2023.pdf application/pdf 1039083 https://riubu.ubu.es/bitstream/10259/9282/1/Ramirez-isat_2023.pdf 3abb32145385ef37d1c4e89378c9bc98 MD5 1 10259/9282 oai:riubu.ubu.es:10259/9282 2024-06-18 02:05:23.924 Repositorio Institucional de la Universidad de Burgos bubrep@ubu.es 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