Reliability assessment of car engine based on dynamic Bayesian network

• Published in: 2016 11th International Conference on Reliability, Maintainability and Safety (ICRMS) pp. 1 - 4
• Main Authors: Wenxue Qian, Qianqian Cao, Jiyao Liu, Xiaowei Yin, Liyang Xie
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-10-2016
• Subjects: Automobiles; Bayes methods; car engine; Dynamic Bayesian Network; Engines; fault detection; multi-state system; Power system reliability; Reliability engineering; Safety; system reliability
• DOI: 10.1109/ICRMS.2016.8050144

A new method based on Dynamic Bayesian Network (DBN) is proposed to assess the car engine reliability because of the limitation that the existing reliability analysis methods can only be used in static system reliability assessment. Firstly, define the failure mode and effect analysis table (FMEA) and the failure rates of the main components of the car engine and construct Bayesian network (BN) model. Secondly, combine the original BN model with time information and assess the reliability of the systems by using the advantages of BN in terms of the multi-state variables and uncertainly relations. The method is able not only to compute the reliability indices of car engine but also to carry out the fault detection easily to recognize the weakness of the system. This research is meaningful for reliability design and prediction of car engine.