SBIR-STTR Award

Structural monitoring of the landing gear of fixed and rotary wing aircraft is of critical importance to the Air Force. These structures are subjected to repeated large dynamic loads that are highly variable in nature. In order to track these structures’ fatigue over time, the magnitude and direction of load events and the number of fatigue cycles must be recorded. Smart, embedded multi-directional load sensors can provide this information. We propose to demonstrate this capability by combining a network of time-synchronized wireless load sensors with integrated microelectronics for static and dynamic loads sensing, data recording, communications, and energy harvesting. Our sensors possess major advantages, including: sealed stainless packaging, full calibration prior to installation, and rapid installation without modification to existing landing gear structures to enable full computation of landing gear forces and moments. Embedded firmware within a wireless sensor data aggregator (WSDA) will automatically detect landing gear deployment, and each node will commence high speed, time synchronized data recording. Data shall be collected prior to, during, and after landing by the WSDA. In Phase II, the WSDA will be programmed to host fatigue algorithms. Energy management combined with energy harvesting will be included to eliminate sensor node battery maintenance.

Benefit:
Our proposed wireless sensors are small, completely self-contained, and may be calibrated independently of the landing gear structure. They may be installed on existing landing gear without any modifications to the gear itself. Working system demonstrations under this SBIR program will lead to significant opportunities aboard Air Force, Navy, Army, Marines, Coast Guard, civil, and commercial aircraft. The potential benefits of our approach are substantiated by the strong support already provided to us by a major US aircraft manufacturer.

Keywords:
Aircraft, Fatigue, Load, Strain, Wireless Sensor Network, Energy Harvesting, Synchronization, Data Aggregation