Weak thruster fault detection for autonomous underwater vehicle based on artificial immune and signal pre-processing

• Published in: Advances in mechanical engineering Vol. 10; no. 2
• Main Authors: Yao, Feng, Wang, Fei, Zhang, Mingjun
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.02.2018; Sage Publications Ltd; SAGE Publishing
• Subjects: Algorithms; Artificial intelligence; Authorship; Autonomous underwater vehicles; Data integration; Detectors; Experiments; False alarms; Fault detection; Fault diagnosis; Feature extraction; Methods; Multisensor fusion; Neural networks; Sensors; Signal processing; Studies; Training; Velocity; China; data fusion; weak thruster fault detection; feature extraction; Autonomous underwater vehicle; artificial immune system
• ISSN: 1687-8132; 1687-8140; 1687-8140
• DOI: 10.1177/1687814018758739

This article investigates weak thruster fault detection problem for autonomous underwater vehicle subject to the external disturbances. A weak thruster fault detection method is developed based on the combination of artificial immune system and single pre-processing. The number of measurable autonomous underwater vehicle’s signals is limited and the weak thruster fault is easy to be compensated by the closed-loop control system. In this developed method, signal pre-processing is first used in the original autonomous underwater vehicle signals, including signal transformation, data fusion based on Dempster–Shafer evidence theory, and feature extraction based on isometric mapping algorithm, to reduce the external disturbance effect and prominently reflect thruster fault. Then artificial immune system is applied to conduct anomaly detection based on the processed autonomous underwater vehicle signals. In the process of fault detection, it includes two stages, training stage and testing stage. In training stage, the detectors are generated at first based on the autonomous underwater vehicle signals in healthy condition based on negative selection and positive selection. And then in testing stage, the generated detectors are used to detect whether fault occurs or not based on the principle of self and non-self discrimination. Finally, to evaluate the performance of the developed fault detection method, Beaver 2 autonomous underwater vehicle is used to conduct experiments. The comparative results demonstrate the effectiveness of the developed method.