The Application of Multi-sensors Fusion in Vehicle Transmission System Fault Diagnosis

Xiaobing Wu; Shuangzhe Liu; Dharmendra Sharma

doi:10.1109/ICNC.2007.709

The Application of Multi-sensors Fusion in Vehicle Transmission System Fault Diagnosis

Xiaobing Wu, Shuangzhe Liu, Dharmendra Sharma

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

Abstract

Multi-sensors fusion technology is adopted for fault diagnosis of vehicle transmission system. By using hybrid pattern fusion based on artificial neural networks (ANN), the robustness of the diagnosing system is improved greatly. This hybrid fusion pattern avoids working with a great deal of original data from sensors, while it has the advantage of less information lost. At the same time, the diagnosis effect is improved by using feature-level and decision-level vibration data and original-level lube data.

Original language	English
Title of host publication	Third International Conference on Natural Computation (ICNC 2007)
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	728-731
Number of pages	4
Volume	2
ISBN (Print)	9780769528755
DOIs	https://doi.org/10.1109/ICNC.2007.709
Publication status	Published - 27 Aug 2007
Event	Third International Conference on Natural Computation (ICNC 2007) - Haikou, China Duration: 24 Aug 2007 → 27 Aug 2007

Conference

Conference	Third International Conference on Natural Computation (ICNC 2007)
Period	24/08/07 → 27/08/07

Access to Document

10.1109/ICNC.2007.709

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4344447

Cite this

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title = "The Application of Multi-sensors Fusion in Vehicle Transmission System Fault Diagnosis",

abstract = "Multi-sensors fusion technology is adopted for fault diagnosis of vehicle transmission system. By using hybrid pattern fusion based on artificial neural networks (ANN), the robustness of the diagnosing system is improved greatly. This hybrid fusion pattern avoids working with a great deal of original data from sensors, while it has the advantage of less information lost. At the same time, the diagnosis effect is improved by using feature-level and decision-level vibration data and original-level lube data.",

keywords = "Vehicles, Fault diagnosis, Sensor fusion, Artificial neural networks, Vibration measurement, Fuses, Data mining, Robustness, Testing, Spectral analysis",

author = "Xiaobing Wu and Shuangzhe Liu and Dharmendra Sharma",

year = "2007",

month = aug,

day = "27",

doi = "10.1109/ICNC.2007.709",

language = "English",

isbn = "9780769528755",

volume = "2",

pages = "728--731",

booktitle = "Third International Conference on Natural Computation (ICNC 2007)",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States",

note = "Third International Conference on Natural Computation (ICNC 2007) ; Conference date: 24-08-2007 Through 27-08-2007",

}

Wu, X, Liu, S & Sharma, D 2007, The Application of Multi-sensors Fusion in Vehicle Transmission System Fault Diagnosis. in Third International Conference on Natural Computation (ICNC 2007). vol. 2, 4344447, IEEE, Institute of Electrical and Electronics Engineers, pp. 728-731, Third International Conference on Natural Computation (ICNC 2007), 24/08/07. https://doi.org/10.1109/ICNC.2007.709

The Application of Multi-sensors Fusion in Vehicle Transmission System Fault Diagnosis. / Wu, Xiaobing; Liu, Shuangzhe ; Sharma, Dharmendra.
Third International Conference on Natural Computation (ICNC 2007). Vol. 2 IEEE, Institute of Electrical and Electronics Engineers, 2007. p. 728-731 4344447.

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

TY - GEN

T1 - The Application of Multi-sensors Fusion in Vehicle Transmission System Fault Diagnosis

AU - Wu, Xiaobing

AU - Liu, Shuangzhe

AU - Sharma, Dharmendra

PY - 2007/8/27

Y1 - 2007/8/27

N2 - Multi-sensors fusion technology is adopted for fault diagnosis of vehicle transmission system. By using hybrid pattern fusion based on artificial neural networks (ANN), the robustness of the diagnosing system is improved greatly. This hybrid fusion pattern avoids working with a great deal of original data from sensors, while it has the advantage of less information lost. At the same time, the diagnosis effect is improved by using feature-level and decision-level vibration data and original-level lube data.

AB - Multi-sensors fusion technology is adopted for fault diagnosis of vehicle transmission system. By using hybrid pattern fusion based on artificial neural networks (ANN), the robustness of the diagnosing system is improved greatly. This hybrid fusion pattern avoids working with a great deal of original data from sensors, while it has the advantage of less information lost. At the same time, the diagnosis effect is improved by using feature-level and decision-level vibration data and original-level lube data.

KW - Vehicles

KW - Fault diagnosis

KW - Sensor fusion

KW - Artificial neural networks

KW - Vibration measurement

KW - Fuses

KW - Data mining

KW - Robustness

KW - Testing

KW - Spectral analysis

UR - https://ieeexplore.ieee.org/document/4344447/

U2 - 10.1109/ICNC.2007.709

DO - 10.1109/ICNC.2007.709

M3 - Conference contribution

SN - 9780769528755

VL - 2

SP - 728

EP - 731

BT - Third International Conference on Natural Computation (ICNC 2007)

PB - IEEE, Institute of Electrical and Electronics Engineers

T2 - Third International Conference on Natural Computation (ICNC 2007)

Y2 - 24 August 2007 through 27 August 2007

ER -

The Application of Multi-sensors Fusion in Vehicle Transmission System Fault Diagnosis

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