Proceedings of the 2016 International Forum on Mechanical, Control and Automation (IFMCA 2016)

Fault identification and analysis of complex electromechanical system using dynamic fault probability

Authors
Hongxia Wu, Jie Han, Dongchen Qin
Corresponding Author
Hongxia Wu
Available Online March 2017.
DOI
https://doi.org/10.2991/ifmca-16.2017.108How to use a DOI?
Keywords
dynamic Bayesian network, dynamic fault probability, complex electromechanical system, fault diagnosis.
Abstract
This paper presents a novel fault identification method based on object oriented Bayesian network (OOBN) and dynamic fault probability according to the structure-function relationship and the failure mode effect analysis (FMEA) of the diagnosed system. Fault identification of complex electromechanical system is difficult. The diagnostic model is in real time because it considers the change of fault probability depending on the working time and the system function state influenced by degradation of component reliability. The peculiarities of proposed approach are that the diagnosis model is hierarchical and dynamic, to depict actual character of nodes. Furthermore, the model is reusable and expandable. The application of ISG-engine in hybrid electric bus demonstrates the effectiveness and superiority of the approach.
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This is an open access article distributed under the CC BY-NC license.

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Volume Title
Proceedings of the 2016 International Forum on Mechanical, Control and Automation (IFMCA 2016)
Series
Advances in Engineering Research
Publication Date
March 2017
ISBN
978-94-6252-307-4
ISSN
2352-5401
DOI
https://doi.org/10.2991/ifmca-16.2017.108How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Hongxia Wu
AU  - Jie Han
AU  - Dongchen Qin
PY  - 2017/03
DA  - 2017/03
TI  - Fault identification and analysis of complex electromechanical system using dynamic fault probability
BT  - Proceedings of the 2016 International Forum on Mechanical, Control and Automation (IFMCA 2016)
PB  - Atlantis Press
SP  - 689
EP  - 695
SN  - 2352-5401
UR  - https://doi.org/10.2991/ifmca-16.2017.108
DO  - https://doi.org/10.2991/ifmca-16.2017.108
ID  - Wu2017/03
ER  -