A Self-Diagnostic Method for Automobile Faults in Multiple Working Conditions Based on SOM-BPNN

Due to the complex and diverse forms of automobile emission detection faults and various interference factors, it is difficult to determine the fault types effectively and accurately use the traditional diagnosis model. In this paper, a multicondition auto fault diagnosis method based on a vehicle c...

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Autores principales:	Zhou Zhou, Xin Cheng, Hui Chang, Jingmei Zhou, Xiangmo Zhao
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/c40b36962749422c8651391ac8b0aa0b
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spelling	oai:doaj.org-article:c40b36962749422c8651391ac8b0aa0b2021-11-15T01:19:05ZA Self-Diagnostic Method for Automobile Faults in Multiple Working Conditions Based on SOM-BPNN1687-527310.1155/2021/6801161https://doaj.org/article/c40b36962749422c8651391ac8b0aa0b2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/6801161https://doaj.org/toc/1687-5273Due to the complex and diverse forms of automobile emission detection faults and various interference factors, it is difficult to determine the fault types effectively and accurately use the traditional diagnosis model. In this paper, a multicondition auto fault diagnosis method based on a vehicle chassis dynamometer is proposed. 3σ method and data normalization were used to pretreat tail gas data. BPNN-RNN (Back Propagation Neural Networks-Recurrent Neural Networks) variable speed integral PID control method was used to achieve high-precision vehicle chassis dynamometer control. Accurate tail gas data were obtained. The simulation and test results of BPNN-RNN variable speed integral PID control were verified and analyzed. The PID control method can quickly adjust PID parameters (within 10 control cycles), control overshoot within 2% of the target value, eliminate the static error, and improve the control performance of the vehicle chassis dynamometer. Combined with BPNN (Back Propagation Neural Network) and SOM (Self-organizing Maps) network, a BPNN-SOM fault diagnosis model is proposed in this paper. By comparing and analyzing the fault diagnosis performance of various neural networks and SOM-BPNN algorithm, it is found that the SOM-BPNN model has the best comprehensive result, the prediction accuracy is 98.75%, the time is 0.45 seconds, and it has good real-time stability. The proposed model can effectively diagnose the vehicle fault, provide a certain direction for maintenance personnel to judge the vehicle state, and provide certain help to alleviate traffic pollution problem.Zhou ZhouXin ChengHui ChangJingmei ZhouXiangmo ZhaoHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7Neurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENComputational Intelligence and Neuroscience, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Zhou Zhou Xin Cheng Hui Chang Jingmei Zhou Xiangmo Zhao A Self-Diagnostic Method for Automobile Faults in Multiple Working Conditions Based on SOM-BPNN
description	Due to the complex and diverse forms of automobile emission detection faults and various interference factors, it is difficult to determine the fault types effectively and accurately use the traditional diagnosis model. In this paper, a multicondition auto fault diagnosis method based on a vehicle chassis dynamometer is proposed. 3σ method and data normalization were used to pretreat tail gas data. BPNN-RNN (Back Propagation Neural Networks-Recurrent Neural Networks) variable speed integral PID control method was used to achieve high-precision vehicle chassis dynamometer control. Accurate tail gas data were obtained. The simulation and test results of BPNN-RNN variable speed integral PID control were verified and analyzed. The PID control method can quickly adjust PID parameters (within 10 control cycles), control overshoot within 2% of the target value, eliminate the static error, and improve the control performance of the vehicle chassis dynamometer. Combined with BPNN (Back Propagation Neural Network) and SOM (Self-organizing Maps) network, a BPNN-SOM fault diagnosis model is proposed in this paper. By comparing and analyzing the fault diagnosis performance of various neural networks and SOM-BPNN algorithm, it is found that the SOM-BPNN model has the best comprehensive result, the prediction accuracy is 98.75%, the time is 0.45 seconds, and it has good real-time stability. The proposed model can effectively diagnose the vehicle fault, provide a certain direction for maintenance personnel to judge the vehicle state, and provide certain help to alleviate traffic pollution problem.
format	article
author	Zhou Zhou Xin Cheng Hui Chang Jingmei Zhou Xiangmo Zhao
author_facet	Zhou Zhou Xin Cheng Hui Chang Jingmei Zhou Xiangmo Zhao
author_sort	Zhou Zhou
title	A Self-Diagnostic Method for Automobile Faults in Multiple Working Conditions Based on SOM-BPNN
title_short	A Self-Diagnostic Method for Automobile Faults in Multiple Working Conditions Based on SOM-BPNN
title_full	A Self-Diagnostic Method for Automobile Faults in Multiple Working Conditions Based on SOM-BPNN
title_fullStr	A Self-Diagnostic Method for Automobile Faults in Multiple Working Conditions Based on SOM-BPNN
title_full_unstemmed	A Self-Diagnostic Method for Automobile Faults in Multiple Working Conditions Based on SOM-BPNN
title_sort	self-diagnostic method for automobile faults in multiple working conditions based on som-bpnn
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/c40b36962749422c8651391ac8b0aa0b
work_keys_str_mv	AT zhouzhou aselfdiagnosticmethodforautomobilefaultsinmultipleworkingconditionsbasedonsombpnn AT xincheng aselfdiagnosticmethodforautomobilefaultsinmultipleworkingconditionsbasedonsombpnn AT huichang aselfdiagnosticmethodforautomobilefaultsinmultipleworkingconditionsbasedonsombpnn AT jingmeizhou aselfdiagnosticmethodforautomobilefaultsinmultipleworkingconditionsbasedonsombpnn AT xiangmozhao aselfdiagnosticmethodforautomobilefaultsinmultipleworkingconditionsbasedonsombpnn AT zhouzhou selfdiagnosticmethodforautomobilefaultsinmultipleworkingconditionsbasedonsombpnn AT xincheng selfdiagnosticmethodforautomobilefaultsinmultipleworkingconditionsbasedonsombpnn AT huichang selfdiagnosticmethodforautomobilefaultsinmultipleworkingconditionsbasedonsombpnn AT jingmeizhou selfdiagnosticmethodforautomobilefaultsinmultipleworkingconditionsbasedonsombpnn AT xiangmozhao selfdiagnosticmethodforautomobilefaultsinmultipleworkingconditionsbasedonsombpnn
_version_	1718429009130291200

A Self-Diagnostic Method for Automobile Faults in Multiple Working Conditions Based on SOM-BPNN

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