Research On Intelligent Information Acquiring And Processing Of Health Monitoring For Great Structure

The goal of structural health monitoring is to achieve real-time monitoring and prediction for the overall behavior of the structure. To achieve this goal, the monitoring system should have a fast large-capacity information collection, transmission and processing capabilities, and data sharing capabilities through network. High-speed data acquisition and transmission, mass data storage and management, monitoring data pre-processing, interpretation, analysis and utilization are the key technical issues to establish the long-term health monitoring system. For the above key technical problems, research on intelligent information acquiring and processing for structural health monitoring are carried out in this paper. The research focuses on the network-based data acquisition and transmission technology, intelligent information processing technology, and dynamic data management and query technology.(1) The guiding ideology of building a structural health monitoring system is refined firstly. Based on the guiding ideology, a general-purpose platform of long-term health monitoring system for large-scale structure is built. The strain test system based on the optic sensor, the electro-sensor test system based on network-based data acquisition and digital temperature sensor testing system are developed independently. The above three test systems, which build up the "small concentration" distributed data acquisition system, are the core of the hardware platform and integrated by the software platform based on the virtual instrument technology and the database system.(2) The usage of wavelet analysis in monitoring information processing for the actual structure is studied. An improved wavelet thresholding algorithm is proposed. The new algorithm, which has a simple expression, performs wave filtering well and improves the quality of noise reduction. The sensitivity of the injury characteristic value expressed by the wavelet packet node energy coefficient and the inherent frequency value after Fourier transform is compared and analyzed. The results show that the injury characteristic value expressed by the wavelet packet node energy coefficient is more sensitive, which proves the usage of wavelet packet in extraction of the injury characteristic value is feasible.(3) The usage of BP neural network in the damage identification for the actual structure monitoring information processing is discussed. A BP network model is established first, and then it is optimized, at last it is used to diagnose the damage of concrete cable-stayed bridge model. The structural damage identification results show that the BP neural network model is easy to identify the damage by the changing of vibration modal frequency.(4) A dynamic data management and query system based on standard relational database is developed independently to implement the unified and standardized management of mass monitoring data. The system provides a good solution to the problems such as mass data storage, data analysis and processing, data query, data backup, data security etc.(5) The above study results have been successfully applied to the Wuhu Yangtze River Bridge and the Zhengzhou Yellow River Bridge, which are two important bridges of symbolic significance. The operation of the two bridge monitoring system shows that the strain test system based on the optic sensor, the electro-sensor test system based on network-based data collection instrument and digital temperature sensor testing system can obtain the corresponding monitoring data well provide intelligent information acquiring and transmitting. The operation of the two bridge monitoring system also shows that wavelet analysis technique selected can complete noise reduction and feature extraction of monitoring data successfully and the BP network model constructed can identify the damage of the structure effectively and the data management and query system developed is able to store monitoring data timely, display query results in real-time and query conveniently. The above technologies achieve analysis, processing, storage and query of monitoring data in real-time and improve the intelligence degree of information processing.The results will further enrich engineering application theories and experimental basis of the structure health monitoring system. And it is of great theoretical significance and practical engineering value. In addition, it will generate significant social and economic benefits.