RT info:eu-repo/semantics/article
T1 Semi-supervised learning for industrial fault detection and diagnosis: A systemic review
A1 Ramírez Sanz, José Miguel
A1 Maestro-Prieto, Jose A.
A1 Arnaiz González, Álvar
A1 Bustillo Iglesias, Andrés
K1 Industrial fault detection and diagnosis
K1 Machine learning
K1 Semi-supervised learning
K1 Systemic review
K1 Vibrations
K1 Informática
K1 Computer science
K1 Bioinformática
K1 Bioinformatics
AB 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.
PB Elsevier
SN 0019-0578
YR 2023
FD 2023-12
LK http://hdl.handle.net/10259/9282
UL http://hdl.handle.net/10259/9282
LA eng
NO 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).
DS Repositorio Institucional de la Universidad de Burgos
RD 29-jun-2024