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Development Of Integrated PZT Sensor Based Structural Health Monitoring Scanning System Software

Posted on:2011-03-07Degree:MasterType:Thesis
Country:ChinaCandidate:B L ZhangFull Text:PDF
GTID:2218330338495934Subject:Instrument Science and Technology
Abstract/Summary:PDF Full Text Request
Structural health monitoring (SHM) technology is a research focus on the engineering domain. System integration is one of the most important research directions in the field. In this thesis, based on the piezoelectric sensor active monitoring methods and system integration technology, integrated PZT sensor based structural health monitoring scanning system software has been developed.The main contents in this thesis are list below:1) According to the requirements of engineering application, the core elements and basic components of software are analyzed. The thesis focuses on the core ideas and key technologies.2) The software modules used to control the hardware has been designed and implemented, which include basic hardware management, advanced hardware management and PZT network management. Advanced hardware management includes multi-channel scanning, fixed channel frequency scanning and hardware self-testing. The software supports the definition and management of sensor networks , achieving a large area of active structural health monitoring.3)The thesis focuses on advanced signal processing technology in structural health monitoring, including signal preprocessing, time domain and frequency domain methods, wavelet transform, pattern recognition and so on. These algorithms have been programmed with C or M language and integrated into the software toolkit. Toolbox can extract the feature parameters and identify structural damage state in time.4)Using multi-channel scanning experiment, sensor network management and multi-channel scanning function have been verified. In screw loose test, the various states of screw loose have been classified by taking feature extraction and pattern recognition.
Keywords/Search Tags:Structural health monitoring, Data acquisition, Signal processing, Vitual instrument, Pattern recognition
PDF Full Text Request
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