Confidence probability cooperation for multi-agent fiber Bragg grating intelligent health monitoring self-healing system

Fiber Bragg Grating (FBG) sensor has attracted considerable attention for intelligent health monitoring system, owing to advantages including resistance to electromagnetic interference, durability under extreme temperature and pressures, light weight, high transmission rate, small size and flexibility. However, the intelligent health monitoring system based on FBG sensor may carry the risk of transmission or sensing optical fiber fracture in the engineering application, the safety and reliability of intelligent health monitoring system will be reduced. For improving the security and reliability, the self-healing implementation of the intelligent health monitoring 25system based on confidence probability cooperation technology is investigated. The self-healing model of multi-agent FBG intelligent health monitoring system based on confidence probability is constructed firstly. Secondly, the optical fiber sensing function agent and system cooperative decision making agent with self-learning ability is defined. The progress of cooperative means between agents based on confidence probability is studied. Thirdly, for the non-participants of the fiber sensing function because of the low confidence probability, dynamic model modification method is studied, and the evaluation results of optical sensing function are modified dynamically. Correspondingly, the system cooperative decision making model is modified on account of its confidence probability. Thus the self-healing ability of the multi-agent FBG intelligent health monitoring system is implemented. Finally, taking the plane wing box test panel as subject, the multi-agent FBG intelligent health monitoring system based on confidence probability is verified and analyzed by experiment and simulation. The results indicate that the multi-agent technology based on confidence probability cooperation not only improves the self-learning ability, but also improves the monitoring accuracy of the FBG intelligent health monitoring system.