Weak thruster fault prediction method for autonomous underwater vehicles based on grey model. (March 2019)

• Record Type: Journal Article
• Authors: Liu, Weixin; Zhang, Mingjun; Wang, Yujia
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 233:Number 3(2019)
• Page Start: 348
• Page End: 356
• Publication Date: 2019-03
• Subjects: Autonomous underwater vehicle -- fault prediction -- weak thruster fault -- grey prediction; Mechanical engineering -- Periodicals; Automatic control -- Periodicals; Systems engineering -- Periodicals; 621.3
• DOI: 10.1177/0959651818793730
• Languages: English
• ISSNs: 0959-6518

Abstract

When adopting the conventional grey model (GM(1, 1)) to predict weak thruster fault for autonomous underwater vehicles, the prediction error is not always satisfactory. In order to solve the problem, this article develops a new weak thruster fault prediction method based on an improved GM(1, 1). In the developed GM(1, 1) based fault prediction method, this article mainly makes improvement in the following aspects: construction of grey background value, solution of whiting differential equation and construction of predicted sequence. Specifically, the integral operation is used in range of the two adjacent steps to obtain the grey background value at first. Second, in the solving of whiting differential equation, the point corresponding to the least difference between the accumulated generation sequence and its predicted sequence is determined, and then this special point's value in the original sequence is considered as the initial condition of the whiting differential equation. Third, in the construction of predicted sequence, another predicted value is obtained based on the error sequence between the accumulated generating operation sequence and its predicted sequence, and then the new predicted result is used to re-adjust the accumulated generating operation sequence, so as to guarantee the re-adjustability of the fault prediction result. Finally, experiments are performed on Beaver 2 autonomous underwater vehicle to evaluate the prediction performance of the developed method.