Intelligent health monitoring self-repairing system and method for fiber grating sensor network

Abstract

The invention discloses an intelligent health monitoring self-repairing system and method for a fiber grating sensor network, belonging to the field of engineering structure health monitoring technology. Aiming at the reliability of an intelligent health monitoring system of a fiber grating sensor in a large-scale engineering structure, the functions of information acquisition, extraction, analysis, evaluation and the like of a conventional distributed fiber grating intelligent health monitoring system are transformed into intelligent agents that can sense the own bodies and the environments and can communicate and mutually cooperate with other agents by adopting multi-agent cooperation and decision-making techniques, failure sensors are subjected to functional compensation based on the fiber grating sensor which is survived near a damaged area through the mutual cooperation among the agents, and thus the whole intelligent health monitoring system can be restored to normal work without changing the fiber grating sensor network in a monitored object, the functional compensation for local failure of the fiber grating sensor network can be achieved, and finally, the intelligent evaluation of the health state of the monitored object can be achieved with higher accuracy and reliability.

Description

technical field [0001] The invention relates to a fiber grating intelligent health monitoring self-repair system and method, belonging to the technical field of engineering structure health monitoring. Background technique [0002] As a new type of sensor, fiber grating sensor has the advantages of sensor integration, light weight, small size, thin diameter, good flexibility, high sensitivity, high resolution, anti-electromagnetic interference, intrinsically safe, and easy to embed compared to traditional sensors. With unique advantages such as medium structure and easy construction of distributed or quasi-distributed networks, it has become one of the most important sensor types in the research and application fields of intelligent health monitoring systems at home and abroad. It has been widely used in large and complex engineering structures such as offshore oil platforms and power systems. [0003] In an intelligent health monitoring system, in order to give full play t...

AI Technical Summary

Problems solved by technology

However, the current research on artificial intelligence algorithm restoration is mainly for small-scale optical fiber sensor networks, the number of sensor devices used in the research is relatively small, the structural parameters to be identified and evaluated are single, and the management and coordination functions between subsystems are relatively simple. , and therefore only suitable as a proof-of-principle and functional proof study

[0007] Considering the actual engineering structure, due to the characteristics of large-scale, complex structure, and various material properties, and the working environment is often in complex and extremely harsh environments such as high speed, high / low temperature, and acid-base corrosion, the intelligent health monitoring system is in actual use. The situation faced in the comprehensive application of engineering becomes particularly complex and difficult, especially the complex and large-scale structure, which will greatly increase the number of sensor devices and the complexity of decision-making objects required in the intelligent health monitoring system. It brings about a sharp increase in the complexity of the monitoring network, the amount of computation used for signal information processing, the information that needs to be communicated and transmitted, the management and coordination requirements between various parts of the system, and the energy consumption required for the monitoring network, which leads to the use of traditional centralized processing This method will not be able to make accurate assessments and correct decisions on the acquired signals in a very short time, and will greatly reduce the real-time diagnosis, real-time control and adaptive capabilities of the monitoring system

Images