Robust Air Data Sensor Fault Diagnosis With Enhanced Fault Sensitivity Using Moving Horizon Estimation
Yiming Wan, Tamas Keviczky, Michel Verhaegen

TL;DR
This paper presents a novel constrained residual generator using moving horizon estimation to improve fault sensitivity in air data sensor fault diagnosis, effectively balancing robustness to winds and fault detection sensitivity.
Contribution
It introduces a constrained residual generator leveraging known wind bounds, enhancing fault sensitivity through active inequality constraints, a novel approach in sensor fault diagnosis.
Findings
Improved fault sensitivity when faults activate inequality constraints.
Enhanced robustness to winds without sacrificing fault detection.
Validated approach using high-fidelity Airbus simulator.
Abstract
This paper investigates robust fault diagnosis of multiple air data sensor faults in the presence of winds. The trade-off between robustness to winds and sensitivity to faults is challenging due to simultaneous influence of winds and latent faults on monitored sensors. Different from conventional residual generators that do not consider any constraints, we propose a constrained residual generator using moving horizon estimation. The main contribution is improved fault sensitivity by exploiting known bounds on winds in residual generation. By analyzing the Karush-Kuhn-Tucker conditions of the formulated moving horizon estimation problem, it is shown that this improvement is attributed to active inequality constraints caused by faults. When the weighting matrices in the moving horizon estimation problem are tuned to increase robustness to winds, its fault sensitivity does not simply…
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Taxonomy
TopicsFault Detection and Control Systems · Advanced Control Systems Optimization · Target Tracking and Data Fusion in Sensor Networks
