2026-04-19T13:50:32Zhttps://riubu.ubu.es/oai/request

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

00925njm 22002777a 4500 dc Ramírez Sanz, José Miguel author Maestro-Prieto, Jose A. author Arnaiz González, Álvar author Bustillo Iglesias, Andrés author 2023-12 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. 0019-0578 http://hdl.handle.net/10259/9282 10.1016/j.isatra.2023.09.027 Industrial fault detection and diagnosis Machine learning Semi-supervised learning Systemic review Vibrations Semi-supervised learning for industrial fault detection and diagnosis: A systemic review