A Neural Algorithm for the Detection and Correction of Anomalies: Application to the Landing of an Airplane

Abstract

The location of the plane is key during the landing operation. A set of sensors provides data to get the best estimation of plane localization. However, data can contain anomalies. To guarantee correct behavior of the sensors, anomalies must be detected. Then, either the faulty sensor is isolated or the detected anomaly is filtered. This article presents a new neural algorithm for the detection and correction of anomalies named NADCA. This algorithm uses a compact deep learning prediction model and has been evaluated using real and simulated anomalies in real landing signals. NADCA detects and corrects both fast-changing and slow-moving anomalies; it is robust regardless of the degree of oscillation of the signals and sensors with abnormal behavior do not need to be isolated. NADCA can detect and correct anomalies in real time regardless of sensor accuracy. Likewise, NADCA can deal with simultaneous anomalies in different sensors and avoid possible problems of coupling between signals. From a technical point of view, NADCA uses a new prediction method and a new approach to obtain a smoothed signal in real time. NADCA has been developed to detect and correct anomalies during the landing of an airplane, hence improving the information presented to the pilot. Nevertheless, NADCA is a general-purpose algorithm that could be useful in other contexts. NADCA evaluation has given an average F-score value of 0.97 for anomaly detection and an average root mean square error (RMSE) value of 2.10 for anomaly correction.

A graph theory-based approach to the description of the process and the diagnostic system

“…They use statistical models (PCA, PLS) (Qin, 2012;Ding, 2014;Jung, 2020;Jakobsson et al, 2020), additive models (Łabęda-Grudziak and Lipiński, 2021) or artificial intelligence models: neural, fuzzy, their combinations, etc. (Patan, 2008;Mrugalski, 2014;Witczak, 2014;Simani et al, 2018;Pulido et al, 2019;Mur et al, 2022;Romero et al, 2022). Such models require training data from the fault-free state of the diagnosed system.…”

Integrated Approach to Diagnostics of Failures and Cyber-Attacks in Industrial Control Systems

“…Fault detection can be carried out using different types of models: analytical [1][2][3], neural [4][5][6], fuzzy [7], and statistical [8,9]. A among them, techniques based on partial parametric models identified in the fault-free state deserve special attention.…”