A Multiagent based Vehicle Engine Fault Diagnosis

Xiaobing Wu; Dharmendra Sharma

A Multiagent based Vehicle Engine Fault Diagnosis

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

Abstract

A special two-level neural network Multiagent system is used in vehicle engine fault diagnosis. Each agent contains a neural network (NN) for its intelligent. The first level is used to acquire the reasons of the fault and the second level is used to classify the fault. There are a few advantages for using two-level NN multiagent system. When new knowledge of faults is acquired, only the first level needs to be retrained. An agent system is added into the second level of the whole system, and the main structure of the network need not be changed. Under the cooperation of the relative agent system, we can diagnose out complex faults and synthetic faults with less resource and better performance

Original language	English
Title of host publication	Knowledge-Based Intelligent Information and Engineering Systems, KES 2007
Editors	B Apollini, R Hewlett, L Jain
Place of Publication	Germany
Publisher	Springer
Pages	541-546
Number of pages	6
ISBN (Print)	9783540748267
Publication status	Published - 2007
Event	KES 2007 - Vietri sul Mare, Italy Duration: 12 Sep 2007 → 14 Sep 2007

Conference

Conference	KES 2007
Country	Italy
City	Vietri sul Mare
Period	12/09/07 → 14/09/07

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Cite this

@inproceedings{abef5257b18e459386e51cbf396dea0a,

title = "A Multiagent based Vehicle Engine Fault Diagnosis",

abstract = "A special two-level neural network Multiagent system is used in vehicle engine fault diagnosis. Each agent contains a neural network (NN) for its intelligent. The first level is used to acquire the reasons of the fault and the second level is used to classify the fault. There are a few advantages for using two-level NN multiagent system. When new knowledge of faults is acquired, only the first level needs to be retrained. An agent system is added into the second level of the whole system, and the main structure of the network need not be changed. Under the cooperation of the relative agent system, we can diagnose out complex faults and synthetic faults with less resource and better performance",

author = "Xiaobing Wu and Dharmendra Sharma",

year = "2007",

language = "English",

isbn = "9783540748267",

pages = "541--546",

editor = "B Apollini and R Hewlett and L Jain",

booktitle = "Knowledge-Based Intelligent Information and Engineering Systems, KES 2007",

publisher = "Springer",

address = "Netherlands",

note = "KES 2007 ; Conference date: 12-09-2007 Through 14-09-2007",

}

TY - GEN

T1 - A Multiagent based Vehicle Engine Fault Diagnosis

AU - Wu, Xiaobing

AU - Sharma, Dharmendra

PY - 2007

Y1 - 2007

N2 - A special two-level neural network Multiagent system is used in vehicle engine fault diagnosis. Each agent contains a neural network (NN) for its intelligent. The first level is used to acquire the reasons of the fault and the second level is used to classify the fault. There are a few advantages for using two-level NN multiagent system. When new knowledge of faults is acquired, only the first level needs to be retrained. An agent system is added into the second level of the whole system, and the main structure of the network need not be changed. Under the cooperation of the relative agent system, we can diagnose out complex faults and synthetic faults with less resource and better performance

AB - A special two-level neural network Multiagent system is used in vehicle engine fault diagnosis. Each agent contains a neural network (NN) for its intelligent. The first level is used to acquire the reasons of the fault and the second level is used to classify the fault. There are a few advantages for using two-level NN multiagent system. When new knowledge of faults is acquired, only the first level needs to be retrained. An agent system is added into the second level of the whole system, and the main structure of the network need not be changed. Under the cooperation of the relative agent system, we can diagnose out complex faults and synthetic faults with less resource and better performance

M3 - Conference contribution

SN - 9783540748267

SP - 541

EP - 546

BT - Knowledge-Based Intelligent Information and Engineering Systems, KES 2007

A2 - Apollini, B

A2 - Hewlett, R

A2 - Jain, L

PB - Springer

CY - Germany

T2 - KES 2007

Y2 - 12 September 2007 through 14 September 2007

ER -