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

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.

Fault detection and prediction scheme for nonlinear stochastic distribution systems

“…In previous studies, fault detection and fault prediction [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15] was carried out separately. There were few papers that carried out fault detection of the system while predicting the failure time of the system.…”

A hybrid fault prediction method for control systems based on extended state observer and hidden Markov model

“…Fault diagnosis and prediction approaches can be broadly divided into two categories: the model-based and model-free methods [7,8]. Model-based approaches use a linear or linearized model of the supervised system to generate a series of fault-indicating signals [9], which rely on the model of the system and prior knowledge of faults.…”