Sensors: Statistical Algorithm Development for Distributed Sensor Networks with Application to Structural Health Monitoring and State Assessment

开发了新的统计和随机振动技术，并应用于解释分布式传感器网络的测量数据。新方法从结构的数据和动态数值模型中建立选定随机变量的统计分布。总体目标是开发方法来识别相对较小的结构损伤或结构状态的变化，在主要结构破坏开始之前。项目内的应用是海上采油结构的隔水管和陆上结构的结构健康监测；这些应用都使用了大量的测量数据。海上应用解决了海洋隔水管的涡激振动问题，这是一个复杂的问题，由于流体-结构相互作用导致系统质量的不规则变化。这些振动是大电流深水区域海洋立管的主要设计考虑因素。陆上应用为监测各种土木结构的结构状况提供了一种改进的方法。这项工作是由德克萨斯农工大学的两个校区（大学城和加尔维斯顿）共同完成的。该项目及其成果将加强两个校区的研究生和本科生工程课程。该项目还直接增加了两个校区之间的互动，这将导致未来更大的合作。该项目由传感器倡议NSF 04-522资助。

ABSTRACT0428585PI: John SweetmanTexas Engineering Experiment StationNew statistical and random vibration techniques are developed and applied to interpret measured data from distributed sensor networks. The new methods build statistical distributions of selected random variables developed from the data and dynamic numerical models of the structure. The overall goal is to develop methodologies to identify relatively minor structural damage or changes in structural state prior to initiation of a major structural failure. Application within the project is marine risers on offshore oil-production structures and to structural health monitoring of onshore structures; each of these applications makes use of extensive measured data. The offshore application addresses vortex induced vibrations of marine risers, a complex problem due to irregular variations in system mass caused by fluid-structure interaction. These vibrations are a dominant design consideration for marine risers in high current, deep-water areas. The onshore application offers an improved methodology for monitoring the structural condition of various civil structures. The work is being done jointly between two campuses of Texas A&M University (College Station and Galveston). The project and its results will enhance the graduate and undergraduate engineering curricula at both campuses. The project also directly increases interaction between the two campuses, which will lead to greater future collaboration. This project is supported under the Sensors Initiative NSF 04-522.