When it comes to hauling heavy loads around, underbuilt equipment will likely break itself, its cargo—and its operator. On the other hand, overbuilt gear is expensive and a waste of material. The same holds true of buildings, bridges, and other structures. But how do you get accurate, real-time data on the way something is performing under variable load conditions?
Instrumenting heavy equipment and structures provides real-time data on their performance under variable load conditions.
SHIELD (Structural Health Integrated Electronic Life Determination) could be the answer. Caterpillar, MicroStrain, Motorola, and Native American Technologies (Golden, CO) have formed a joint venture to investigate the use of wireless sensing neural networks as a way to improve risk and failure management as well as structural efficiency. The three-year project will develop and demonstrate prototype sensors and analysis technologies to determine in real time the condition and remaining functional life of large pieces of equipment and/or structures. The University of Illinois at Urbana-Champaign and Drexel University will serve as consultants on damage analysis calculations and structural health monitoring, respectively.
The SHIELD system will consist of wireless sensors that can be attached to different parts of a structure to collect data continuously on actual use, fatigue damage, cracks, and other parameters, and hardware and software to analyze these data. The low-cost, low-power sensors will be based on microelectromechanical systems. The researchers will develop hardware for high-speed damage calculations and write software to accurately determine the loads of dynamic systems. Neural networks will supplement the dynamic structual analyses.
When deployed, SHIELD will improve safety and reduce losses by predicting potential catastrophic failures, enhance productivity through optimized structural design and operation, and shrink maintenance and repair costs by monitoring damage accumulation. In addition to its initial applications in mining and construction equipment, the system could be used to monitor trucks, bridges, buildings, aircraft, railways, and ships.
The project is supported by a $4.4 million grant from NIST's Advanced Technology Program, with matching funds from the participating companies.