Aircraft robust data-driven multiple sensor fault diagnosis based on optimality criteria. (1st May 2022)

• Record Type: Journal Article
• Authors: Cartocci, Nicholas; Napolitano, Marcello R.; Costante, Gabriele; Valigi, Paolo; Fravolini, Mario L.
• Is Part Of: Mechanical systems and signal processing. Volume 170(2022)
• Publication Date: 2022-05-01
• Subjects: AR Analytical Redundancy -- ARRs Analytical Redundancy Relations -- CDF Cumulative Distribution Function -- C-MAR Compact-Multiple Analytic Redundancy -- FD Fault Detection -- FDi Fault Diagnosis -- FDIE Fault Detection-Isolation-Estimation -- FE Fault Estimation -- FI Fault Isolation -- LS Least Squares -- MAR Multiple Analytic Redundancy -- MARRs Multiple Analytic Redundancy Relations -- PCs Principal Components -- pre-FI pre-Fault Isolation -- RBCs Reconstruction-Based Contributions -- RNS Right Null Space -- R-SPE Robust-Squared Prediction Error -- SMVR Self-adaptive Multiple-Variable Reconstruction -- SNR Signal-to-Noise Ratio -- SoA State-of-the-Art -- SPE Squared Prediction Error -- STD Standard Deviation -- SVD Singular Value Decomposition -- TDR True Detection Rate -- TIR True Isolation Rate; Multiple-Fault Diagnosis -- Data-Driven -- Aircraft -- Directional residuals -- Optimal robust residuals -- Analytical Redundancy; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2021.108668
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: Complete data-based design of a multiple sensor fault isolation system based on fault free data. Capability of handling robust multiple fault isolation through the design of optimized Analytical Redundancy Relations. Validation studies on actual flight data show increased effectiveness in isolating multiple faults compared to state-of-the-art methods. General method applicable to the robust sensor fault diagnosis of any dynamic system. 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.