Aircraft robust data-driven multiple sensor fault diagnosis based on optimality criteria

Abstract

A general robust data-driven scheme for the Fault Detection, Isolation and Estimation of multiple sensor faults is proposed and validated using multi-flight data records. Robustness to modelling uncertainty and noise is achieved through an optimized data-driven design of the three blocks that constitute the scheme. First, a robust Fault Detection (FD) filter given by the linear combination of previously identified Analytical Redundancy Relationships (AARs) is derived as the solution of a multi-objective optimization where the minimum fault sensitivity is maximized while the standard deviation (STD) of the filtered error, in nominal condition, is minimized. Then, a Fault Pre-Isolation (FPI) block is introduced to select a restricted number of sensors containing with high likelihood the subset of the faulty sensors. In this phase, robustness is achieved through the data-driven design of a redundant number of Multiple-ARRs and a voting logic. Finally, the robust Fault Isolation (FI) is achieved relying on the design of a large collection of additional AARs whose fault signatures are specifically designed to optimize, at the same time, noise immunity while maximizing the decoupling of the (pre-isolated) fault directions. A procedure based on fault amplitude reconstruction is proposed to isolate the set of faulty sensors sequentially. The proposed scheme has been applied to the design of a multiple Fault Diagnosis scheme for a set of 8 sensors of a semi-autonomous aircraft basing on multi-flight data. Validation results are compared with state-of-the-art multiple Fault Diagnosis schemes.