A Novel Wireless Sensor Network with Advanced Prognostic Algorithms for Condition Based Maintenance of Critical Power Plant Components
Improving efficiency, reducing emissions, and reducing costs are key objectives of power plants. The achievement of these objectives will require the development of two mutually dependent functions: distributed data acquisition and real-time data interpretation. This project will combine a wireless sensor network (WSN) with an advanced diagnostic and prognostic capability to monitor and assess critical power plant components. In Phase I, two test beds were constructed: one for emulating electrical faults and one for emulating mechanical faults. Real-time experiments were performed, with wireless data collection used in all experiments. Real-time health monitoring algorithms were developed, and actual bearing data were used to validate the algorithms. Phase II will implement all of the diagnostic and prognostics tools in a real-time processing unit. Real-time field tests will be carried out with a WSN to collect various sensor data. Commercial Applications and Other Benefits as described by the awardee: The technology should find use in any application (DOE, NASA, military, or commercial) involving electrical and mechanical components. Specific applications include turbine engines, bearings, pumps, gearboxes, motors, and generators.
Small Business Information at Submission:
Signal Processing, Inc.
13619 Valley Oak Circle Rockville, MD 20850
Number of Employees: