Auto-NAHL: A Neural Network Approach for Condition-Based Maintenance of Complex Industrial Systems

Nowadays, machine learning has emerged as a promising alternative for condition monitoring of industrial processes, making it indispensable for maintenance planning. Such a learning model is able to assess health states in real time provided that both training and testing samples are complete and ha...

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Autores principales:	Tarek Berghout, Mohamed Benbouzid, S. M. Muyeen, Toufik Bentrcia, Leila-Hayet Mouss
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Compressed sensing condition monitoring fault detection hydraulic systems industrial system machine learning Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/8a95c28b3d8741c397aacfeff34073e6
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id	oai:doaj.org-article:8a95c28b3d8741c397aacfeff34073e6
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spelling	oai:doaj.org-article:8a95c28b3d8741c397aacfeff34073e62021-11-20T00:02:26ZAuto-NAHL: A Neural Network Approach for Condition-Based Maintenance of Complex Industrial Systems2169-353610.1109/ACCESS.2021.3127084https://doaj.org/article/8a95c28b3d8741c397aacfeff34073e62021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9610082/https://doaj.org/toc/2169-3536Nowadays, machine learning has emerged as a promising alternative for condition monitoring of industrial processes, making it indispensable for maintenance planning. Such a learning model is able to assess health states in real time provided that both training and testing samples are complete and have the same probability distribution. However, it is rare and difficult in practical applications to meet these requirements due to the continuous change in working conditions. Besides, conventional hyperparameters tuning via grid search or manual tuning requires a lot of human intervention and becomes inflexible for users. Two objectives are targeted in this work. In an attempt to remedy the data distribution mismatch issue, we firstly introduce a feature extraction and selection approach built upon correlation analysis and dimensionality reduction. Secondly, to diminish human intervention burdens, we propose an Automatic artificial Neural network with an Augmented Hidden Layer (Auto-NAHL) for the classification of health states. Within the designed network, it is worthy to mention that the novelty of the implemented neural architecture is attributed to the new multiple feature mappings of the inputs, where such configuration allows the hidden layer to learn multiple representations from several random linear mappings and produce a single final efficient representation. Hyperparameters tuning including the network architecture, is fully automated by incorporating Particle Swarm Optimization (PSO) technique. The designed learning process is evaluated on a complex industrial plant as well as various classification problems. Based on the obtained results, it can be claimed that our proposal yields better response to new hidden representations by obtaining a higher approximation compared to some previous works.Tarek BerghoutMohamed BenbouzidS. M. MuyeenToufik BentrciaLeila-Hayet MoussIEEEarticleCompressed sensingcondition monitoringfault detectionhydraulic systemsindustrial systemmachine learningElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 152829-152840 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Compressed sensing condition monitoring fault detection hydraulic systems industrial system machine learning Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Compressed sensing condition monitoring fault detection hydraulic systems industrial system machine learning Electrical engineering. Electronics. Nuclear engineering TK1-9971 Tarek Berghout Mohamed Benbouzid S. M. Muyeen Toufik Bentrcia Leila-Hayet Mouss Auto-NAHL: A Neural Network Approach for Condition-Based Maintenance of Complex Industrial Systems
description	Nowadays, machine learning has emerged as a promising alternative for condition monitoring of industrial processes, making it indispensable for maintenance planning. Such a learning model is able to assess health states in real time provided that both training and testing samples are complete and have the same probability distribution. However, it is rare and difficult in practical applications to meet these requirements due to the continuous change in working conditions. Besides, conventional hyperparameters tuning via grid search or manual tuning requires a lot of human intervention and becomes inflexible for users. Two objectives are targeted in this work. In an attempt to remedy the data distribution mismatch issue, we firstly introduce a feature extraction and selection approach built upon correlation analysis and dimensionality reduction. Secondly, to diminish human intervention burdens, we propose an Automatic artificial Neural network with an Augmented Hidden Layer (Auto-NAHL) for the classification of health states. Within the designed network, it is worthy to mention that the novelty of the implemented neural architecture is attributed to the new multiple feature mappings of the inputs, where such configuration allows the hidden layer to learn multiple representations from several random linear mappings and produce a single final efficient representation. Hyperparameters tuning including the network architecture, is fully automated by incorporating Particle Swarm Optimization (PSO) technique. The designed learning process is evaluated on a complex industrial plant as well as various classification problems. Based on the obtained results, it can be claimed that our proposal yields better response to new hidden representations by obtaining a higher approximation compared to some previous works.
format	article
author	Tarek Berghout Mohamed Benbouzid S. M. Muyeen Toufik Bentrcia Leila-Hayet Mouss
author_facet	Tarek Berghout Mohamed Benbouzid S. M. Muyeen Toufik Bentrcia Leila-Hayet Mouss
author_sort	Tarek Berghout
title	Auto-NAHL: A Neural Network Approach for Condition-Based Maintenance of Complex Industrial Systems
title_short	Auto-NAHL: A Neural Network Approach for Condition-Based Maintenance of Complex Industrial Systems
title_full	Auto-NAHL: A Neural Network Approach for Condition-Based Maintenance of Complex Industrial Systems
title_fullStr	Auto-NAHL: A Neural Network Approach for Condition-Based Maintenance of Complex Industrial Systems
title_full_unstemmed	Auto-NAHL: A Neural Network Approach for Condition-Based Maintenance of Complex Industrial Systems
title_sort	auto-nahl: a neural network approach for condition-based maintenance of complex industrial systems
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/8a95c28b3d8741c397aacfeff34073e6
work_keys_str_mv	AT tarekberghout autonahlaneuralnetworkapproachforconditionbasedmaintenanceofcomplexindustrialsystems AT mohamedbenbouzid autonahlaneuralnetworkapproachforconditionbasedmaintenanceofcomplexindustrialsystems AT smmuyeen autonahlaneuralnetworkapproachforconditionbasedmaintenanceofcomplexindustrialsystems AT toufikbentrcia autonahlaneuralnetworkapproachforconditionbasedmaintenanceofcomplexindustrialsystems AT leilahayetmouss autonahlaneuralnetworkapproachforconditionbasedmaintenanceofcomplexindustrialsystems
_version_	1718419846666911744

Auto-NAHL: A Neural Network Approach for Condition-Based Maintenance of Complex Industrial Systems

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