Research On FBG Sensing For Engineering Applications

Fiber Bragg grating (FBG) sensors have a number of distinguishing advantages. The FBG sensor devices are simple, intrinsic, distributing sensing elements and have all the advantages normally attributed to fiber sensors, such as high sensitivity, EMI immunity, waterproof and anti-erode. With the improvement of signal demodulation and analyses, FBG sensors have been wildly researched in many domains, such civil projects, spaceflight and navigation, electric power, petroleum and medical treatment. High precision digital FBG sensor signal demodulation systems have wildly practical future in healthy inspection of bridge structure, and it's also important research of FBG sensors technology recently. Distribution of FBG sensors network and the management of a large bulk of signals need to set up a FBG intelligent structure. There is a new direction of research about information fusion method of sensors signals using neural network technology. With the introduction of neural network technology, the FBG sensors intelligent structures and systems will maximally develop to practicality and systematism as soon as possible. The main contents and key creation points of this dissertation are as follows:(1) Designed a digital FBG sensor all-spectrum signals scan scheme, and developed the relevant equipment. Through the noise analyses in progress of FBG reflection spectrum signals transmission and collection, we introduced digital matched filter (DMF) and digital zero-phase filer (DZF), made arithmetic analyses and simulated the system using Labview software. We carried out strain test using cantilever fabric in Lab, and contrasted the two digital filter technologies. The system precision can reach to1με, and the whole structure is better than other practical FBG sensor systems in common.(2) Presented a information fusion method of FBG signals using neural network technology, and made theoretical analyses and experiment simulation. The application of FBG sensor and neural network in strain damage inspection, in which fiber Bragg grating was used as sensing element, concrete simple supporting plank was investigated as moulding board, and neural network set up by Matlab was utilized as signal processing means. We sampled the discrete strain signals and fused the information to deduce the load value reversely by means of neural network. Experimental results illustrates that in the load range of 0KN to110KN, the relative error of sample identification is under 3%, Mean Square Error(MSE) is less than 1kN.(3) In the course of bridge building, FBG sensor network was laid in root-style pier to make structure inspection. Self-balance method was applied in devastating experiments. We applied neural network in this FBG sensor network project. Through analysis of the pier strain spots distribution by finite element method, we positioned the FBG sensor array. In experiment, we load the pier for 24 hours and record the FBG sensor reflection spectrum. Through experiment results and neural network technology, we bring forward a concept of inspecting the pier load by subarea, and judge the security range. The relative error of sample identification is under 5%.(4) Make a tentative study of FBG sensor error compensate from temperature using neural network information fusion arithmetic. Input strain signals as aim information by adjust cantilever, input temperature signals as non-aim information by adjust temperature circulator. We set temperature range for three part, the central temperature is 5oC ,25oC ,45oC respectively, and each part have±5oC fluctuate. After error compensate by neural network arithmetic, Mean Square Error(MSE) of FBG sensor signals is less than 5με, 10-20 times than error compensate before. The research indicated that neural network is applicable to FBG strain sensors temperature compensate, and offer the basis for future study.