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.

中文翻译：

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通过LASSO优化和贝叶斯滤波的鲁棒故障检测和隔离残差基于数据的设计

本文提出了一种基于数据的方法，用于结构化残差子集的设计，以可靠地隔离传感器故障。线性回归模型用于估计故障信号并建立一组主要残差。大号₁正规化最小二乘估计用于识别模型参数并通过增加正规化权重来增强解决方案的稀疏性。以此方式，可以产生具有不同故障敏感性的一组残差发生器。然后，提出了一种基于故障敏感性最大化的残差选择程序，以提取结构化残差的最小尺寸子集，从而可以隔离故障传感器。为了克服建模的不确定性，已采用鲁棒的递归贝叶斯滤波器在线处理残差与标称故障方向之间的距离，从而为每个传感器提供故障概率。通过使用P92 Tecnam飞机的多次飞行实验数据为六个飞机传感器设计和测试故障隔离方案，已验证了该方法的有效性。