Study On Data Discrimination Process Of Health Monitoring In Road And Bridge Structure

At present, health monitoring system of road and bridge structures is one of the mostpopular research directions in the world. And in the health monitoring system, the mostimportant part is the analysis of monitoring data. In addition, especially for the wirelesssensor networks, the data transfer efficiency is the key to the success of the entire healthmonitoring system. Before the wireless transmission of monitoring data, the discriminationof monitoring data can effectively reduce the amount of data to be transported and also canimprove the efficiency of the transmission, so that it can ensure the real-time andeffectiveness of monitoring and warning system. In China, the study of automated structuralhealth monitoring platform is from the late1990s. Although we have obtained some goodachievements, for the adaptive discrimination processing of monitoring data is not to besatisfied.In order to solve the adaptive discrimination processing of monitoring data during themonitoring process and avoid the transmission channel lag phenomenon due to the mass ofmonitoring data which would affect the real-time and effectiveness of warning platform, wehave analyzed the monitoring process of slope, subgrade and bridges on the basis of readinglots of relevant literatures. Then, we study on the features of monitoring parameters of thethree structure above combined with the National High Technology Research andDevelopment Program ("863"Program) of China named "The research of wide range seasonfrozen road disasters parameter monitoring and identification of warning system" andaccording to the research topics in the data discrimination requirements.1. We analyze the monitoring process of slope, subgrade and bridge structure. Themonitoring parameters of these structure are studied. Then the frame of data discriminationwere determined.2. ARMA data discrimination algorithm based on modern time series model has beendecided, and the setting up and solving process is derived in detail and elaboration. Slopedisplacement, subgrade settlement of the real-time monitoring data adaptive discriminationprocess were established. Feasibility and effectiveness of this method are verified throughlaboratory tests and practical engineering.3. Dynamic response of the gray relational theory-based bridge monitoring data discrimination is established, and the discrimination model and solution procedure areanalyzed in detail. Adaptive discrimination of bridge frequency data is realized. In the modelbuilding process, the use of binary regression analysis of gray relational grade of thereal-time frequency data corrected to standard temperature, thus excluding the impact ofoutside temperature change. This method is applied to the Changchun City, Silicon Valleyoverpass monitoring projects, to verify the feasibility and effectiveness of discrimination onthe bridge dynamic response data.And on the adaptive discrimination processing of monitoring data, we first save themonitoring data into the database of base station computer. This stored procedure is finishedby the transmission line connected with monitoring sensors and base station computer.Secondly, we realize the adaptive discrimination processing of monitoring data byprogramming in the base station computer. Finally, after the data discrimination, for themonitoring data need to be send to the warning platform, we use3G Wireless Networks toconduct the transmission process. After data discrimination, the invalid monitoring data isstored in base station computer for some time, and then it would be automatically cleared toensure that the base station computer has enough space to store monitoring data andcomplete the process of data discrimination. On the algorithm of data adaptivediscrimination processing, after comparative study of a variety methods of datadiscrimination, we decide that the slope displacement data and subgrade settlement data areusing time series methods to data discrimination. However, the bridge frequency data areusing the gray grey correlation method to discriminate.Before the discrimination of displacement data of slope and subgrade, we must do somepretreatment to the original monitoring data collected through the monitoring sensors in filedso that we can ensure continuity and accuracy of monitoring data. Because slope andsubgrade structure is also in a stable state for a long time, and the rope type displacementmeter and precision single-point displacement meter are buried deep into the slope andsubgrade, respectively, the monitoring data of the two meters is relatively accurate and lesssubject to outside influence. On this basis, we believe that the two structural monitoring datais not much affected by noises. Thus, we use simple denoising method to preprocess the rawmonitoring data. At the same time, because solar power supply may lead to voltageinstability and for other objective reasons, it will generally have a monitoring sensor leakagephenomenology: the data acquisition is not the equal time interval. So for the nature ofmonitoring data could not be changed, we need to fill the data which is lacking. In this paper, we use lagrange interpolation method to fill the data. After the pretreatment process above,we need to test the stationarity of the monitoring data. Under normal circumstances, themonitoring data are non-steady time series. We usually use the differential approach to treatthe monitoring data sequence and then test the self-correlation coefficient in order to the datasequence has become stationary time series. Then we can use it for the ARMA modeling.During the ARMA modeling of monitoring data discrimination, the author use the solutionof how to solve the parameter to derive the formula of ARMA modeling and describe thesolution process in detail. Finally, we use the derived formula for programming. Then, thismethod is validated in the room test and obtains a good discriminate effect. After that, weapply the method to Changchun Metro West Railway Station and State Road "102" linemonitoring project and it is also has achieved good results.In discrimination of bridge monitoring data, we first calculate the bridge frequencythrough the FFT transform using the acceleration response data. Then, we use greycorrelation algorithm to compare the bridge frequency with the initial state of bridge. We usethe monitoring data as the comparison sequence and the frequency of the initial state of thebridge structure as the original sequence. After comparison, if gray relational degree of themonitoring is greater than the threshold frequency, we regard it as the safety data and won'tsend it wirelessly. On the opposite side, we send the data whose relational degree is less thanthe threshold frequency to the warning platform to judge. We use the method above forprogramming in order to realize the adaptive discrimination of bridge monitoring data andwe do the indoor experiments. After achieving better results in the laboratory test, we use itfor the bridge monitoring in Changchun City and have a good monitoring effect.