A data-driven fault detection approach for Modular Reconfigurable Flying Array based on the Improved Deep Forest

Abstract

The Modular Reconfigurable Flying Array, as a kind of special modular rotorcraft UAV, can change its topology configurations fitting for different tasks and varying work scenarios. However, strongly nonlinear characteristics and limited uncertainties of Modular Reconfigurable Flying Array make its sensor faults happen easily and fail to be detected with higher accuracy. In the light of the potential serious losses of its sensors faults, it is valuable and challenging to detect the sensor faults of the aircraft accurately and effectively. Therefore, a data-driven multivariate regression approach based on the Improved Deep Forest is proposed to fulfill sensor faults detection. Firstly, the Deep Forest algorithm is improved by adding the enhanced cascade layer structure and redesigning the inter-layer loss function to pursuit better prediction accuracy. And then, the Improved Deep Forest algorithm is used to establish a multivariate regression model and obtains an estimation of the monitored parameter by learning the historical flight data. Finally, the residual between the actual flight data and the estimated value is calculated to achieve sensor faults detection by comparing with the statistical threshold. What is more, the proposed faults detection approach is evaluated with two real flight datasets collected from the self-made rotorcraft and the experimental results show that the average ACC and AUC are increased by 3% and 2.3% respectively compared with the approach based on the standard Deep Forest.