Fault Prediction of Brightness Sensor based on BRB in Rail Vehicle Compartment LED Lighting System

Abstract

To guarantee the normal workflow and accurate brightness adjustment, it is important to predict fault of brightness sensor in rail vehicle compartment LED lighting system. In this paper, a BRB (belief rule base) based fault prediction model is proposed to accurate brightness adjustment and reliability based on the analysis of the failure mechanism of the brightness sensor in the rail vehicle compartment LED lighting system. The fault prediction model based on BRB can make full use of the system's expert prior knowledge, which can fuse the system feature quantity to achieve accurate fault prediction of the brightness sensor. In this process, the parameters of the model are updated by iterative estimation algorithm to compensate for the inaccuracy of expert knowledge. Finally, in order to verify the validity and accuracy of the proposed model, a case is studied by using the proposed prediction model for brightness sensor module in the rail vehicle compartment LED lighting system, which shows that the method can accurately predict the faults with qualitative knowledge and quantitative information.