Extending Point-sensor Performance by Incorporating Distributed-sensors and Window Functions
Resource
Journal of Intelligent Material Systems and Structures 16 (2): 149-161
Journal
Journal of Intelligent Material Systems and Structures
Journal Volume
16
Journal Issue
2
Pages
149-161
Date Issued
2005-02
Date
2005-02
Author(s)
Abstract
By integrating the methodologies of window functions and the method of image, the performance of the traditional piezoelectric point and distributed sensors, such as accelerometers, can be tailored by modifying their transfer functions in the spatial domain. The approach used in extending the sensor performance consists of developing a series of methodologies that can tailor the gain of the sensor transfer function while keeping the phase of the sensor transfer function intact. We can show that these spatially introduced design methodologies bypass the Bode gain-phase theorem, which states that gain and phase are interrelated for all minimum phase systems. The effects of adopting these sensors to flexible structure control and point sensors are examined in detail. It can also be shown that a series of low-pass filters, which exerts autonomous behavior between the gain and the phase of the sensor transfer function, can be introduced to significantly influence the performance of the sensing or controlling loops that incorporates these sensors. In addition, variables such as the power of the window functions adopted, the desired corner frequency, the length of the sensor structure, etc., all can be shown to have a decisive impact on the performance of the newly invented sensors. Both theoretical and experimental results of the underlying principles, design methodologies, and implementation process of these newly invented sensors are detailed. © 2005, Sage Publications. All rights reserved.
Subjects
accelerometer; distributed sensor; piezoelectricity; point sensor; window function
Type
journal article
File(s)![Thumbnail Image]()
Loading...
Name
34.pdf
Size
313.08 KB
Format
Adobe PDF
Checksum
(MD5):29d0e0d17394d50f88c99d356993762c