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Sensors 2017, 17(4), 835; doi:10.3390/s17040835

Robust In-Flight Sensor Fault Diagnostics for Aircraft Engine Based on Sliding Mode Observers

Jiangsu Province Key Laboratory of Aerospace Power System, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
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Received: 12 March 2017 / Revised: 5 April 2017 / Accepted: 7 April 2017 / Published: 11 April 2017
(This article belongs to the Section Physical Sensors)
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Abstract

For a sensor fault diagnostic system of aircraft engines, the health performance degradation is an inevitable interference that cannot be neglected. To address this issue, this paper investigates an integrated on-line sensor fault diagnostic scheme for a commercial aircraft engine based on a sliding mode observer (SMO). In this approach, one sliding mode observer is designed for engine health performance tracking, and another for sensor fault reconstruction. Both observers are employed in in-flight applications. The results of the former SMO are analyzed for post-flight updating the baseline model of the latter. This idea is practical and feasible since the updating process does not require the algorithm to be regulated or redesigned, so that ground-based intervention is avoided, and the update process is implemented in an economical and efficient way. With this setup, the robustness of the proposed scheme to the health degradation is much enhanced and the latter SMO is able to fulfill sensor fault reconstruction over the course of the engine life. The proposed sensor fault diagnostic system is applied to a nonlinear simulation of a commercial aircraft engine, and its effectiveness is evaluated in several fault scenarios. View Full-Text
Keywords: commercial aircraft engine; health degradation; sensor fault diagnostics; sliding mode observer commercial aircraft engine; health degradation; sensor fault diagnostics; sliding mode observer
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Chang, X.; Huang, J.; Lu, F. Robust In-Flight Sensor Fault Diagnostics for Aircraft Engine Based on Sliding Mode Observers. Sensors 2017, 17, 835.

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