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Modeling, detection, and disambiguation of sensor faults for aerospace applications
| Content Provider | NASA Technical Reports Server (NTRS) |
|---|---|
| Author | Balaban, Edward Saxena, Abhinav Curran, Simon Bansal, Prasun Goebel, Kai F. |
| Copyright Year | 2009 |
| Description | Sensor faults continue to be a major hurdle for systems health management to reach its full potential. At the same time, few recorded instances of sensor faults exist. It is equally difficult to seed particular sensor faults. Therefore, research is underway to better understand the different fault modes seen in sensors and to model the faults. The fault models can then be used in simulated sensor fault scenarios to ensure that algorithms can distinguish between sensor faults and system faults. The paper illustrates the work with data collected from an electro-mechanical actuator in an aerospace setting, equipped with temperature, vibration, current, and position sensors. The most common sensor faults, such as bias, drift, scaling, and dropout were simulated and injected into the experimental data, with the goal of making these simulations as realistic as feasible. A neural network based classifier was then created and tested on both experimental data and the more challenging randomized data sequences. Additional studies were also conducted to determine sensitivity of detection and disambiguation efficacy to severity of fault conditions. |
| File Size | 8782374 |
| Page Count | 11 |
| File Format | |
| Alternate Webpage(s) | http://archive.org/details/NASA_NTRS_Archive_20090033812 |
| Archival Resource Key | ark:/13960/t2j72f46j |
| Language | English |
| Publisher Date | 2009-01-01 |
| Access Restriction | Open |
| Subject Keyword | Electronics And Electrical Engineering Electromechanical Devices Actuators Classifiers Systems Management Simulation Detection Sequencing Aerospace Engineering Ntrs Nasa Technical Reports Server (ntrs) Nasa Technical Reports Server Aerodynamics Aircraft Aerospace Aeronautic Space Science |
| Content Type | Text |
| Resource Type | Article |
