A roadmap to fault diagnosis of industrial machines via machine learning: A brief review

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F25%3A10256521" target="_blank" >RIV/61989100:27230/25:10256521 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/abs/pii/S0263224124021018" target="_blank" >https://www.sciencedirect.com/science/article/abs/pii/S0263224124021018</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.measurement.2024.116216" target="_blank" >10.1016/j.measurement.2024.116216</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A roadmap to fault diagnosis of industrial machines via machine learning: A brief review

Popis výsledku v původním jazyce

In fault diagnosis, machine learning theories are gaining popularity as they proved to be an efficient tool that not only reduces human effort but also identifies the health conditions of the machines automatically. In this work, an attempt has been made to systematically review the progress of machine learning theories in fault diagnosis from scratch to future perspectives. Initially, artificial intelligence came into the picture which started to weaken the human effort whose efficiency relies on feature extraction which depends on expert knowledge. The introduction of deep learning theories has reformed the fault diagnosis process by realising the artificial aid, encouraging end-to-end encryption in the diagnostic procedure. The deep learning theories have also filled the gap between the large amount of monitoring data and the health conditions of industrial machines. The future of deep learning theories i.e. transfer learning which uses the knowledge of one domain to another related domain during fault diagnosis has been reviewed. In last, the research trends of the machine learning theories have been briefly discussed along with their challenges in fault diagnostics. (C) 2024 Elsevier Ltd

Název v anglickém jazyce

A roadmap to fault diagnosis of industrial machines via machine learning: A brief review

Popis výsledku anglicky

In fault diagnosis, machine learning theories are gaining popularity as they proved to be an efficient tool that not only reduces human effort but also identifies the health conditions of the machines automatically. In this work, an attempt has been made to systematically review the progress of machine learning theories in fault diagnosis from scratch to future perspectives. Initially, artificial intelligence came into the picture which started to weaken the human effort whose efficiency relies on feature extraction which depends on expert knowledge. The introduction of deep learning theories has reformed the fault diagnosis process by realising the artificial aid, encouraging end-to-end encryption in the diagnostic procedure. The deep learning theories have also filled the gap between the large amount of monitoring data and the health conditions of industrial machines. The future of deep learning theories i.e. transfer learning which uses the knowledge of one domain to another related domain during fault diagnosis has been reviewed. In last, the research trends of the machine learning theories have been briefly discussed along with their challenges in fault diagnostics. (C) 2024 Elsevier Ltd

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20301 - Mechanical engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Measurement

ISSN

0263-2241

e-ISSN

1873-412X

Svazek periodika

242

Číslo periodika v rámci svazku

116216

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

28

Strana od-do

nestránkováno

Kód UT WoS článku

001359921300001

EID výsledku v databázi Scopus

2-s2.0-85209254329