In this paper, a data-based approach for the design of structured residual subsets for the robust isolation of sensor faults is proposed. Linear regression models are employed to estimate faulty signals and to build a set of primary residuals. L1-regularized least squares estimation is used to identify model parameters and to enforce sparsity of the solutions by increasing the regularization weight. In this way, it is possible to generate a set of residuals generators with different fault sensitivity. Then, a residual selection procedure based on fault sensitivity maximization is proposed to extract a minimum size subset of structured residuals that allows for isolation of the faulty sensor. To overcome modelling uncertainty, a robust recursive Bayesian Filter has been employed to process, online, the distance of the residuals from nominal fault directions, providing a fault probability for each sensor. The proposed method has been validated by designing and testing a fault isolation scheme for six aircraft sensors using multi-flight experimental data of a P92 Tecnam aircraft.

Data-based design of robust fault detection and isolation residuals via LASSO optimization and Bayesian filtering / Cascianelli, S.; Costante, G.; Crocetti, F.; Ricci, E.; Valigi, P.; Luca Fravolini, M.. - In: ASIAN JOURNAL OF CONTROL. - ISSN 1561-8625. - ELETTRONICO. - 23:1(2021), pp. 57-71. [10.1002/asjc.2392]

Data-based design of robust fault detection and isolation residuals via LASSO optimization and Bayesian filtering

Ricci E.;
2021-01-01

Abstract

In this paper, a data-based approach for the design of structured residual subsets for the robust isolation of sensor faults is proposed. Linear regression models are employed to estimate faulty signals and to build a set of primary residuals. L1-regularized least squares estimation is used to identify model parameters and to enforce sparsity of the solutions by increasing the regularization weight. In this way, it is possible to generate a set of residuals generators with different fault sensitivity. Then, a residual selection procedure based on fault sensitivity maximization is proposed to extract a minimum size subset of structured residuals that allows for isolation of the faulty sensor. To overcome modelling uncertainty, a robust recursive Bayesian Filter has been employed to process, online, the distance of the residuals from nominal fault directions, providing a fault probability for each sensor. The proposed method has been validated by designing and testing a fault isolation scheme for six aircraft sensors using multi-flight experimental data of a P92 Tecnam aircraft.
2021
1
Cascianelli, S.; Costante, G.; Crocetti, F.; Ricci, E.; Valigi, P.; Luca Fravolini, M.
Data-based design of robust fault detection and isolation residuals via LASSO optimization and Bayesian filtering / Cascianelli, S.; Costante, G.; Crocetti, F.; Ricci, E.; Valigi, P.; Luca Fravolini, M.. - In: ASIAN JOURNAL OF CONTROL. - ISSN 1561-8625. - ELETTRONICO. - 23:1(2021), pp. 57-71. [10.1002/asjc.2392]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/284406
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