Autonomous Under Water Vehicle Based on Extreme Learning Machine for Sensor Fault Diagnosistics. (2020)

• Record Type: Journal Article
• Authors: Subha, T.D.; Subash, TD.; Claudia Jane, K.S.; Devadharshini, D.; Francis, Dhanya I.
• Is Part Of: Materials today. Volume 24:Part 4(2020)
• Page Start: 2394
• Page End: 2402
• Publication Date: 2020
• Subjects: Autonomous underwater vehicle -- sensor faults diagnosis -- phase space reconstruction -- extreme learning machine; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2020.03.769
• Languages: English
• ISSNs: 2214-7853

Abstract

Abstract: The autonomous under water vehicle (AUV) system uses sensors which have a very important role in this field, even work in very complex environment. In order to improve the reliability of the AUV systems it important to know about the sensor failure diagnostic technology. In this paper two methods has been proposed they are combining phase space reconstruction and extreme learning machine(ELM).They are mostly being used to predict the sensor output in order to achieve the sensor fault diagnosis for AUVS. This stimulation experiments based on sea trial data shows the proposed method can be diagnosed by the sensor faults and recover the signals after the faults occur in a period of time