Fault identification and analysis of complex electromechanical system using dynamic fault probability
Hongxia Wu, Jie Han, Dongchen Qin
Available Online March 2017.
- https://doi.org/10.2991/ifmca-16.2017.108How to use a DOI?
- dynamic Bayesian network, dynamic fault probability, complex electromechanical system, fault diagnosis.
- 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.
- 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 -