A machine learning approach for gearbox system fault diagnosis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22340%2F21%3A43922915" target="_blank" >RIV/60461373:22340/21:43922915 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21220/21:00352800

Výsledek na webu

<a href="https://www.mdpi.com/1099-4300/23/9/1130/htm" target="_blank" >https://www.mdpi.com/1099-4300/23/9/1130/htm</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/e23091130" target="_blank" >10.3390/e23091130</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A machine learning approach for gearbox system fault diagnosis

Popis výsledku v původním jazyce

This study proposes a fully automated gearbox fault diagnosis approach that does not require knowledge about the specific gearbox construction and its load. The proposed approach is based on evaluating an adaptive filter’s prediction error. The obtained prediction error’s standard deviation is further processed with a support-vector machine to classify the gearbox’s condition. The proposed method was cross-validated on a public dataset, segmented into 1760 test samples, against two other reference methods. The accuracy achieved by the proposed method was better than the accuracies of the reference methods. The accuracy of the proposed method was on average 9% higher compared to both reference methods for different support vector settings. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Název v anglickém jazyce

A machine learning approach for gearbox system fault diagnosis

Popis výsledku anglicky

This study proposes a fully automated gearbox fault diagnosis approach that does not require knowledge about the specific gearbox construction and its load. The proposed approach is based on evaluating an adaptive filter’s prediction error. The obtained prediction error’s standard deviation is further processed with a support-vector machine to classify the gearbox’s condition. The proposed method was cross-validated on a public dataset, segmented into 1760 test samples, against two other reference methods. The accuracy achieved by the proposed method was better than the accuracies of the reference methods. The accuracy of the proposed method was on average 9% higher compared to both reference methods for different support vector settings. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EF16_019%2F0000826" target="_blank" >EF16_019/0000826: Centrum pokročilých leteckých technologií</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

Entropy

ISSN

1099-4300

e-ISSN

—

Svazek periodika

23

Číslo periodika v rámci svazku

9

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

15

Strana od-do

—

Kód UT WoS článku

000700218600001

EID výsledku v databázi Scopus

2-s2.0-85114255985