Fault Detection in the MSW Incineration Process Using Stochastic Configuration Networks and Case-Based Reasoning

Fault detection in the waste incineration process depends on high-temperature image observation and the experience of field maintenance personnel, which is inefficient and can easily cause misjudgment of the fault. In this paper, a fault detection method is proposed by combining stochastic configura...

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Autores principales:	Chenxi Ding, Aijun Yan
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	MSW incineration process fault detection case-based reasoning stochastic configuration networks learning pseudo-metric Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/3f946c23f57c42d09b6dc7511381532e
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spelling	oai:doaj.org-article:3f946c23f57c42d09b6dc7511381532e2021-11-11T19:17:50ZFault Detection in the MSW Incineration Process Using Stochastic Configuration Networks and Case-Based Reasoning10.3390/s212173561424-8220https://doaj.org/article/3f946c23f57c42d09b6dc7511381532e2021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7356https://doaj.org/toc/1424-8220Fault detection in the waste incineration process depends on high-temperature image observation and the experience of field maintenance personnel, which is inefficient and can easily cause misjudgment of the fault. In this paper, a fault detection method is proposed by combining stochastic configuration networks (SCNs) and case-based reasoning (CBR). First, a learning pseudo metric method based on SCNs (SCN-LPM) is proposed by training SCN learning models using a training sample set and defined pseudo-metric criteria. Then, the SCN-LPM method is used for the case retrieval stage in CBR to construct the fault detection model based on SCN-CBR, and the structure, algorithmic implementation, and algorithmic steps are given. Finally, the performance is tested using historical data of the MSW incineration process, and the proposed method is compared with typical classification methods, such as a Back Propagation (BP) neural network, a support vector machine, and so on. The results show that this method can effectively improve the accuracy of fault detection and reduce the time complexity of the task and maintain a certain application value.Chenxi DingAijun YanMDPI AGarticleMSW incineration processfault detectioncase-based reasoningstochastic configuration networkslearning pseudo-metricChemical technologyTP1-1185ENSensors, Vol 21, Iss 7356, p 7356 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	MSW incineration process fault detection case-based reasoning stochastic configuration networks learning pseudo-metric Chemical technology TP1-1185
spellingShingle	MSW incineration process fault detection case-based reasoning stochastic configuration networks learning pseudo-metric Chemical technology TP1-1185 Chenxi Ding Aijun Yan Fault Detection in the MSW Incineration Process Using Stochastic Configuration Networks and Case-Based Reasoning
description	Fault detection in the waste incineration process depends on high-temperature image observation and the experience of field maintenance personnel, which is inefficient and can easily cause misjudgment of the fault. In this paper, a fault detection method is proposed by combining stochastic configuration networks (SCNs) and case-based reasoning (CBR). First, a learning pseudo metric method based on SCNs (SCN-LPM) is proposed by training SCN learning models using a training sample set and defined pseudo-metric criteria. Then, the SCN-LPM method is used for the case retrieval stage in CBR to construct the fault detection model based on SCN-CBR, and the structure, algorithmic implementation, and algorithmic steps are given. Finally, the performance is tested using historical data of the MSW incineration process, and the proposed method is compared with typical classification methods, such as a Back Propagation (BP) neural network, a support vector machine, and so on. The results show that this method can effectively improve the accuracy of fault detection and reduce the time complexity of the task and maintain a certain application value.
format	article
author	Chenxi Ding Aijun Yan
author_facet	Chenxi Ding Aijun Yan
author_sort	Chenxi Ding
title	Fault Detection in the MSW Incineration Process Using Stochastic Configuration Networks and Case-Based Reasoning
title_short	Fault Detection in the MSW Incineration Process Using Stochastic Configuration Networks and Case-Based Reasoning
title_full	Fault Detection in the MSW Incineration Process Using Stochastic Configuration Networks and Case-Based Reasoning
title_fullStr	Fault Detection in the MSW Incineration Process Using Stochastic Configuration Networks and Case-Based Reasoning
title_full_unstemmed	Fault Detection in the MSW Incineration Process Using Stochastic Configuration Networks and Case-Based Reasoning
title_sort	fault detection in the msw incineration process using stochastic configuration networks and case-based reasoning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/3f946c23f57c42d09b6dc7511381532e
work_keys_str_mv	AT chenxiding faultdetectioninthemswincinerationprocessusingstochasticconfigurationnetworksandcasebasedreasoning AT aijunyan faultdetectioninthemswincinerationprocessusingstochasticconfigurationnetworksandcasebasedreasoning
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Fault Detection in the MSW Incineration Process Using Stochastic Configuration Networks and Case-Based Reasoning

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