Aircraft Gearbox Fault Diagnosis System: An Approach based on Deep Learning Techniques

Gearbox is one of the vital components in aircraft engines. If any small damage to gearbox, it can cause the breakdown of aircraft engine. Thus it is significant to study fault diagnosis in gearbox system. In this paper, two deep learning models (Long short term memory (LSTM) and Bi-directional long...

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Autores principales:	Mallikarjuna P B, Sreenatha M, Manjunath S, Kundur Niranjan C
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2020
Materias:	gearbox vibration data long short term memory bi-directional long short term memory Science Q Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/17446db051b9492f92f42460381b8a3e
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id	oai:doaj.org-article:17446db051b9492f92f42460381b8a3e
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spelling	oai:doaj.org-article:17446db051b9492f92f42460381b8a3e2021-12-05T14:10:51ZAircraft Gearbox Fault Diagnosis System: An Approach based on Deep Learning Techniques2191-026X10.1515/jisys-2019-0237https://doaj.org/article/17446db051b9492f92f42460381b8a3e2020-08-01T00:00:00Zhttps://doi.org/10.1515/jisys-2019-0237https://doaj.org/toc/2191-026XGearbox is one of the vital components in aircraft engines. If any small damage to gearbox, it can cause the breakdown of aircraft engine. Thus it is significant to study fault diagnosis in gearbox system. In this paper, two deep learning models (Long short term memory (LSTM) and Bi-directional long short term memory (BLSTM)) are proposed to classify the condition of gearbox into good or bad. These models are applied on aircraft gearbox vibration data in both time and frequency domain. A publicly available aircraft gearbox vibration dataset is used to evaluate the performance of proposed models. The results proved that accuracy achieved by LSTM and BLSTM are highly reliable and applicable in health monitoring of aircraft gearbox system in time domain as compared to frequency domain. Also, to show the superiority of proposed models for aircraft gearbox fault diagnosis, performance is compared with classical machine learning models.Mallikarjuna P BSreenatha MManjunath SKundur Niranjan CDe Gruyterarticlegearboxvibration datalong short term memorybi-directional long short term memoryScienceQElectronic computers. Computer scienceQA75.5-76.95ENJournal of Intelligent Systems, Vol 30, Iss 1, Pp 258-272 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	gearbox vibration data long short term memory bi-directional long short term memory Science Q Electronic computers. Computer science QA75.5-76.95
spellingShingle	gearbox vibration data long short term memory bi-directional long short term memory Science Q Electronic computers. Computer science QA75.5-76.95 Mallikarjuna P B Sreenatha M Manjunath S Kundur Niranjan C Aircraft Gearbox Fault Diagnosis System: An Approach based on Deep Learning Techniques
description	Gearbox is one of the vital components in aircraft engines. If any small damage to gearbox, it can cause the breakdown of aircraft engine. Thus it is significant to study fault diagnosis in gearbox system. In this paper, two deep learning models (Long short term memory (LSTM) and Bi-directional long short term memory (BLSTM)) are proposed to classify the condition of gearbox into good or bad. These models are applied on aircraft gearbox vibration data in both time and frequency domain. A publicly available aircraft gearbox vibration dataset is used to evaluate the performance of proposed models. The results proved that accuracy achieved by LSTM and BLSTM are highly reliable and applicable in health monitoring of aircraft gearbox system in time domain as compared to frequency domain. Also, to show the superiority of proposed models for aircraft gearbox fault diagnosis, performance is compared with classical machine learning models.
format	article
author	Mallikarjuna P B Sreenatha M Manjunath S Kundur Niranjan C
author_facet	Mallikarjuna P B Sreenatha M Manjunath S Kundur Niranjan C
author_sort	Mallikarjuna P B
title	Aircraft Gearbox Fault Diagnosis System: An Approach based on Deep Learning Techniques
title_short	Aircraft Gearbox Fault Diagnosis System: An Approach based on Deep Learning Techniques
title_full	Aircraft Gearbox Fault Diagnosis System: An Approach based on Deep Learning Techniques
title_fullStr	Aircraft Gearbox Fault Diagnosis System: An Approach based on Deep Learning Techniques
title_full_unstemmed	Aircraft Gearbox Fault Diagnosis System: An Approach based on Deep Learning Techniques
title_sort	aircraft gearbox fault diagnosis system: an approach based on deep learning techniques
publisher	De Gruyter
publishDate	2020
url	https://doaj.org/article/17446db051b9492f92f42460381b8a3e
work_keys_str_mv	AT mallikarjunapb aircraftgearboxfaultdiagnosissystemanapproachbasedondeeplearningtechniques AT sreenatham aircraftgearboxfaultdiagnosissystemanapproachbasedondeeplearningtechniques AT manjunaths aircraftgearboxfaultdiagnosissystemanapproachbasedondeeplearningtechniques AT kundurniranjanc aircraftgearboxfaultdiagnosissystemanapproachbasedondeeplearningtechniques
_version_	1718371660881461248

Aircraft Gearbox Fault Diagnosis System: An Approach based on Deep Learning Techniques

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