Within the context of preventive health maintenance in complex engineering systems, novel sensor fault detection methodologies are developed for an aircraft auxiliary power unit. Promising results at operational and sensor failure conditions are obtained for temperature and pressure sensors. In the methodology proposed, first covariance and noise analyses of sensor data are performed. Next, auto- associative and hetero-associative neural networks for sensor validation are designed and trained. These neural networks are used together to provide validation for pressure and temperature sensors. The last step consists of development of detection and identification logic for sensor faults. In spite o high noise levels, the methodology is shown to be very robust. More than 90% correct sensor failure detection is achieved when noise on the order of noise inherently present in sensor readings is added.

Date Published: 20 July 2001
PDF: 8 pages
Proc. SPIE 4389, Component and Systems Diagnostics, Prognosis, and Health Management, (20 July 2001); doi: 10.1117/12.434229

Published in SPIE Proceedings Vol. 4389:
Component and Systems Diagnostics, Prognosis, and Health Management
Peter K. Willett; Thiagalingam Kirubarajan, Editor(s)