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The state-of-practice for aircraft manufacturers to diagnose guidance & control faults and obtain full flight envelope protection at all times is to provide high levels of dissimilar hardware redundancy. This ensures sufficient available control action and allows performing coherency tests, cross and consistency checks, voting mechanisms and built-in test techniques of varying sophistication. This hardware-redundancy based fault detection and diagnosis (FDD) approach is nowadays the standard industrial practice and fits also into current aircraft certification processes while ensuring the highest level of safety standards. In the context of future “sustainable” aircraft (More Affordable, Smarter, Cleaner and Quieter), the Electrical Flight Control System (EFCS) design objectives, originating from structural loads design constraints, are becoming more and more stringent.

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.