Aeronautical relay health state assessment model based on belief rule base with attribute reliability

作者:

Highlights:

摘要

Health state assessment is a key issue in health management of aeronautical relay subject to complex interference environment. The input reliability of assessment model has direct connection with the assessment result and the reliability of the assessment model. Due to the limitation of resources and monitoring technology, it is impossible to simultaneously improve the reliabilities of all the characteristics. Thus, some important characteristics should be sorted by the role they play in the assessment model. Implementing the quantitatively analysis of the influence of the input reliability can provide guidance. The belief rule base model with attribute reliability (BRB-r) provides such a modeling framework and analysis method. It is one of the expert systems that can aggregate unreliable quantitative data and expert knowledge and has traceability between the model input and output. Thus, in this paper, a new health state assessment model based on BRB-r for aeronautical relay is developed for the first time where the calculation method of model reliability is further developed. Then, to quantitatively analyze the effectiveness of the input reliability on the model output and the model reliability, the sensitivity analysis of attribute reliability is deduced based on the first-order local sensitivity method. The obtained sensitivity coefficient of attribute reliability represents its effectiveness on the constructed health state assessment model and can provide guidance in health management for aeronautical relay under limited resource. A case study of health sate estimation of the JRC-7M aeronautical relay is conducted to illustrate the application of the new model.

论文关键词:Health state assessment,Aeronautical relay,Sensitivity analysis,Belief rule base (BRB),Attribute reliability

论文评审过程:Received 8 October 2019, Revised 1 April 2020, Accepted 2 April 2020, Available online 9 April 2020, Version of Record 24 April 2020.

论文官网地址:https://doi.org/10.1016/j.knosys.2020.105869