Energy Harvesting for Structural Health Monitoring Sensor Networks

TLDR

This paper reviews the development of energy harvesting for low‑power embedded structural health monitoring sensing systems and introduces a statistical pattern recognition paradigm for data acquisition. It surveys existing and emerging SHM sensing modalities, their power needs, network architectures, power‑optimization strategies, and discusses various energy‑harvesting and storage techniques along with current technological limitations. The authors conclude by outlining future research needed to move energy‑harvesting SHM systems from laboratory to field prototypes and note that the discussed technologies are broadly applicable to any low‑power embedded sensor.

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.