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This paper discusses the design of a model-based fault detection scheme for robust and early detection of runaways in aircraft control surfaces servo-loop. The proposed scheme can be embedded within the structure of in-service monitoring systems as a part of the Flight Control Computer (FCC) software. The final goal is to contribute to improve the performance detection of unanticipated runaway faulty profiles having very different dynamic behaviors, while retaining a perfect robustness. The paper discusses also the tradeoffs between adequacy of the technique and its implementation level, industrial validation process with Engineering support tools, as well as the tuning aspects. The proposed methodology is based on a combined data-driven and system-based approach using a dedicated Kalman filtering. The technique provides an effective method ensuring robustness and good performance (well-defined real-time characteristics and well-defined error rates).

In the framework of the aircraft global optimization, for future and upcoming programs, current research interests include more Electrical Flight Control System (EFCS) autonomy for a more easy-to-handle aircraft. A possible solution is to increase the number of redundant flight parameter sensors but to the detriment of the aircraft weight and so to the cost and performances. This paper proposes an algorithm using PLS (Partial Least Squares) to estimate a flight parameter from independent sensor measurements. The estimates are then used as so-called “software” or “virtual” sensors, allowing aircraft weight saving. This algorithm is based on an iterative processing and thus can be used in real time in the embedded flight control computer. Furthermore, the resulting flight parameter estimates can be used to detect failures. Different detection strategies are proposed and results show that this method can lead to robust detections.