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Energy Harvesting for Structural Health Monitoring Sensor Networks

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TLDR
Some future research directions that are aimed at transitioning the concept of energy harvesting for embedded SHM sensing systems from laboratory research to field-deployed engineering prototypes are defined.
Abstract
This paper reviews the development of energy harvesting for low-power embedded structural health monitoring (SHM) sensing systems. A statistical pattern recognition paradigm for SHM is first presented and the concept of energy harvesting for embedded sensing systems is addressed with respect to the data acquisition portion of this paradigm. Next, various existing and emerging sensing modalities used for SHM and their respective power requirements are summarized followed by a discussion of SHM sensor network paradigms, power requirements for these networks, and power optimization strategies. Various approaches to energy harvesting and energy storage are discussed and limitations associated with the current technology are addressed. The paper concludes by defining some future research directions that are aimed at transitioning the concept of energy harvesting for embedded SHM sensing systems from laboratory research to field-deployed engineering prototypes. Finally, it is noted that many of the technologies discussed herein are applicable to powering any type of low-power embedded sensing system regardless of the application.

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Energy Harvesting for
Structural Health Monitoring Sensor Networks
LA-14314-MS
Approved for public release;
distribution is unlimited.

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About the cover—Energy Harvesting Example: A piezoelectric patch converts ambient
mechanical vibration into useful electrical energy. The electrical charge from the
piezoelectric material is first rectified and then stored in a capacitor until sufficient energy
is accumulated to power a commercially available thermocouple. The capacitor becomes
sufficiently charged in less than 10 s of low-ampitude vibration.

Energy Harvesting for
Structural Health Monitoring Sensor Networks
G. Park
C. R. Farrar
M. D. Todd*
W. Hodgkiss**
T. Rosing†
* Department of Structural Engineering, University of California, San Diego,
La Jolla, CA 92093-0085.
** Department of Electrical and Computer Engineering, University of California, San Diego,
La Jolla CA 92093-0701.
Department of Computer Science and Engineering, University of California, San Diego,
La Jolla, CA 92093-0114.
LA-14314-MS
Issued: February 2007


Energy Harvesting for SHM Sensor Networks LA-14314-MS
v
CONTENTS
LIST OF FIGURES ..................................................................................................................... vii
LIST OF TABLES....................................................................................................................... viii
ENERGY HARVESTING FOR STRUCTURAL HEALTH MONITORING SENSOR
NETWORKS ............................................................................................................................... 1
ABSTRACT................................................................................................................................. 1
1. INTRODUCTION ................................................................................................................. 1
1.1. The Structural Health Monitoring Process .................................................................... 2
1.2. Annual Workshops ........................................................................................................ 3
2. SENSING SYSTEM DESIGN CONSIDERATIONS FOR SHM ........................................ 4
2.1. SHM Sensing Overview and Introduction .................................................................... 4
2.2. SHM Sensor Networks .................................................................................................. 5
2.3. Sensor Modalities in Current SHM System Use ........................................................... 6
2.3.1. Acceleration........................................................................................................ 7
2.3.2. Strain................................................................................................................... 11
2.3.3. Fiber-Optic Strain Sensing ................................................................................. 13
2.3.4. Piezoelectric Sensor/Actuators ........................................................................... 15
3. CURRENT SHM SENSOR NETWORK PARADIGMS ..................................................... 17
3.1. Wired System ................................................................................................................ 18
3.2. Wireless Transmission Systems .................................................................................... 19
3.3. Sensor Network Paradigms ........................................................................................... 22
3.3.1. Sensor Arrays Directly Connected to Central Processing Hardware ................. 22
3.3.2. Decentralized Sensing and Processing with Hopping Connection..................... 22
3.3.3. Decentralized Active Sensing and Processing with Hybrid Connection............ 23
3.4. Future Sensing Network Paradigms .............................................................................. 25
3.5. Practical Implementation Issues for SHM Sensing Networks ...................................... 26
3.6. Sensor Properties ........................................................................................................... 26
3.7. Sensor Calibration and Ruggedness .............................................................................. 26
3.8. Multiscale Sensing......................................................................................................... 27
3.9. Power Consideration ..................................................................................................... 28

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Related Papers (5)
Frequently Asked Questions (3)
Q1. What have the authors contributed in "Energy harvesting for structural health monitoring sensor networks" ?

In this paper, the authors proposed a dynamic voltage scaling ( DVS ) algorithm for real-time systems. 

This section outlines future research areas for energy harvesting in order to transition the current state-of-the-art to full-scale deployment in the current practice of SHM and sensing networks. Another exciting possibility is the emerging technology of flexible, thin-film batteries [ 170 ] or power-fiber batteries [ 171 ] that can be fully integrated into energy harvesting mediums, forming the concept of structural batteries or harvesting batteries. Therefore, efficient and innovative methods of storing electrical energy are the key technologies that will allow energy harvesting to become a source of power for electronics and wireless sensors. Ultracapacitors possess the ability to deliver bursts of high power, can be recharged rapidly from any energy source, and are capable Energy Harvesting for SHM Sensor Networks LA-14314-MS 59 over 600,000 charge cycles [ 84 ]. 

Introduction ................................................................................................................... 28 4.2. Dynamic Power Management ....................................................................................... 32 4.3. Heuristic Policies........................................................................................................... 334.3.1. Timeout Policies ................................................................................................. 33 4.3.2. Predictive Policies .............................................................................................. 344.4. Stochastic Policies ......................................................................................................... 34 4.5. Operating System and Cross-Layer Dynamic Power Management .............................. 36 4.6. Dynamic Voltage Scaling.............................................................................................. 37 4.7. Intertask Voltage Scaling .............................................................................................. 37 4.8. Intra-Task Voltage Scaling............................................................................................ 38 4.9. Conclusion.....................................................................................................................