Scalable, Energy Harvesting, Wireless Sensor Network for Structural Health Monitoring of Ships

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00014-10-M-0194
Agency Tracking Number: N101-095-1427
Amount: $69,903.00
Phase: Phase I
Program: SBIR
Awards Year: 2010
Solicitation Year: 2010
Solicitation Topic Code: N101-095
Solicitation Number: 2010.1
Small Business Information
MicroStrain, Inc.
459 Hurricane Lane, Suite 102, Williston, VT, 05495
DUNS: 161852074
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Steven Arms
 President
 (802) 862-6629
 swarms@microstrain.com
Business Contact
 Sarah Lynch
Title: Contracts Specialist
Phone: (802) 862-6629
Email: slynch@microstrain.com
Research Institution
N/A
Abstract
Structural health monitoring (SHM) of large structures such as Navy ships requires an in depth knowledge of operational loads and how these loads may change over time. A network of low cost, wireless strain sensors can provide this information. During Phase I, we will demonstrate a highly synchronized, scalable network of energy harvesting wireless strain sensors. By converting ambient cyclic strains into power, these sensors can monitor operational loads for the lifetime of the ship without battery replacement. Synchronized strain, temperature, shock, vibration, and loads data may be collected at high data rates. Timing synchronization and data collection is orchestrated with a wired network of Ethernet enabled wireless controllers and wireless sensor data aggregators (WSDAs). The wireless sensing network uses time division multiple access to control communications and to reduce power consumption through duty cycling. The network is organized in critical structural clusters such that at least one node in the cluster is always actively sampling. In the Phase I Option, we will demonstrate that during relatively low loading conditions, energy is conserved and stored. In contrast, during relatively high levels of accumulated damage or when overloads are detected, the network automatically adjusts its operating modes to become more vigilant.

* information listed above is at the time of submission.

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