Application of Model-Based Deep Learning Algorithm in Fault Diagnosis of Coal Mills

Application of Model-Based Deep Learning Algorithm in Fault Diagnosis of Coal Mills

The coal mill is one of the important auxiliary engines in the coal-fired power station. Its operation status is directly related to the safe and steady operation of the units. In this paper, a model-based deep learning algorithm for fault diagnosis is proposed to effectively detect the operation st...

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Main Authors: Yifan Jian, Xianguo Qing, Yang Zhao, Liang He, Xiao Qi
Format: Article
Language:English
Published: Hindawi Limited 2020-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2020/3753274
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spelling doaj-4c3bbaf33b544cddb3de002ef6fa5a152020-11-25T03:42:23ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472020-01-01202010.1155/2020/37532743753274Application of Model-Based Deep Learning Algorithm in Fault Diagnosis of Coal MillsYifan Jian0Xianguo Qing1Yang Zhao2Liang He3Xiao Qi4Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, ChinaScience and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, ChinaScience and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, ChinaScience and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, ChinaEnergy and Electricity Research Center, Jinan University, Zhuhai, Guangdong, ChinaThe coal mill is one of the important auxiliary engines in the coal-fired power station. Its operation status is directly related to the safe and steady operation of the units. In this paper, a model-based deep learning algorithm for fault diagnosis is proposed to effectively detect the operation state of coal mills. Based on the system mechanism model of coal mills, massive fault data are obtained by analyzing and simulating the different types of faults. Then, stacked autoencoders (SAEs) are established by combining the said data with the deep learning algorithm. The SAE model is trained by the fault data, which provide it with the learning and identification capability of the characteristics of faults. According to the simulation results, the accuracy of fault diagnosis of coal mills based on SAE is high at 98.97%. Finally, the proposed SAEs can well detect the fault in coal mills and generate the warnings in advance.http://dx.doi.org/10.1155/2020/3753274
collection DOAJ
language English
format Article
sources DOAJ
author Yifan Jian
Xianguo Qing
Yang Zhao
Liang He
Xiao Qi
spellingShingle Yifan Jian
Xianguo Qing
Yang Zhao
Liang He
Xiao Qi
Application of Model-Based Deep Learning Algorithm in Fault Diagnosis of Coal Mills
Mathematical Problems in Engineering
author_facet Yifan Jian
Xianguo Qing
Yang Zhao
Liang He
Xiao Qi
author_sort Yifan Jian
title Application of Model-Based Deep Learning Algorithm in Fault Diagnosis of Coal Mills
title_short Application of Model-Based Deep Learning Algorithm in Fault Diagnosis of Coal Mills
title_full Application of Model-Based Deep Learning Algorithm in Fault Diagnosis of Coal Mills
title_fullStr Application of Model-Based Deep Learning Algorithm in Fault Diagnosis of Coal Mills
title_full_unstemmed Application of Model-Based Deep Learning Algorithm in Fault Diagnosis of Coal Mills
title_sort application of model-based deep learning algorithm in fault diagnosis of coal mills
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1024-123X
1563-5147
publishDate 2020-01-01
description The coal mill is one of the important auxiliary engines in the coal-fired power station. Its operation status is directly related to the safe and steady operation of the units. In this paper, a model-based deep learning algorithm for fault diagnosis is proposed to effectively detect the operation state of coal mills. Based on the system mechanism model of coal mills, massive fault data are obtained by analyzing and simulating the different types of faults. Then, stacked autoencoders (SAEs) are established by combining the said data with the deep learning algorithm. The SAE model is trained by the fault data, which provide it with the learning and identification capability of the characteristics of faults. According to the simulation results, the accuracy of fault diagnosis of coal mills based on SAE is high at 98.97%. Finally, the proposed SAEs can well detect the fault in coal mills and generate the warnings in advance.
url http://dx.doi.org/10.1155/2020/3753274
work_keys_str_mv AT yifanjian applicationofmodelbaseddeeplearningalgorithminfaultdiagnosisofcoalmills
AT xianguoqing applicationofmodelbaseddeeplearningalgorithminfaultdiagnosisofcoalmills
AT yangzhao applicationofmodelbaseddeeplearningalgorithminfaultdiagnosisofcoalmills
AT lianghe applicationofmodelbaseddeeplearningalgorithminfaultdiagnosisofcoalmills
AT xiaoqi applicationofmodelbaseddeeplearningalgorithminfaultdiagnosisofcoalmills
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