Weak thruster fault prediction method for autonomous underwater vehicles based on grey model

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.

[1]  Yuan Xu,et al.  Time Series Extended Finite‐State Machine‐Based Relevance Vector Machine Multi‐Fault Prediction , 2017 .

[2]  Richard Dearden,et al.  Automated Fault Diagnosis for an Autonomous Underwater Vehicle , 2013, IEEE Journal of Oceanic Engineering.

[3]  Santanu Kumar Rath,et al.  Effective fault prediction model developed using Least Square Support Vector Machine (LSSVM) , 2017, J. Syst. Softw..

[4]  Xiong Wei,et al.  Dynamic customer requirements analysis based on the improved grey forecasting model , 2010 .

[5]  Gwyn Griffiths,et al.  Risk Analysis for Autonomous Underwater Vehicle Operations in Extreme Environments , 2010, Risk analysis : an official publication of the Society for Risk Analysis.

[6]  Alexey Zhirabok,et al.  Observer based fault diagnosis in thrusters of autonomous underwater vehicle , 2010, 2010 Conference on Control and Fault-Tolerant Systems (SysTol).

[7]  Mingjun Zhang,et al.  Thruster fault feature extraction for autonomous underwater vehicle in time-varying ocean currents based on single-channel blind source separation , 2016, J. Syst. Control. Eng..

[8]  Mohd Rizal Arshad,et al.  Diagnosis of thruster fault condition using statistical design of experiment , 2012 .

[9]  Zhou Dong,et al.  Review of Intermittent Fault Diagnosis Techniques for Dynamic Systems , 2014 .

[10]  Xing Liu,et al.  Adaptive fault tolerant control and thruster fault reconstruction for autonomous underwater vehicle , 2018 .

[11]  Zheng Bin.  Improvement and Application of Grey Prediction GM(1,1) Model , 2011 .

[12]  Maheshkumar H. Kolekar,et al.  Stator winding fault prediction of induction motors using multiscale entropy and grey fuzzy optimization methods , 2014, Comput. Electr. Eng..

[13]  Xu Chun-hong,et al.  Review of incipient fault diagnosis methods , 2012 .

[14]  Robert Sutton,et al.  Neural network fault diagnosis of a trolling motor based on feature reduction techniques for an unmanned surface vehicle , 2015, J. Syst. Control. Eng..

[15]  Lai Yinan.  Basic Research on Machinery Fault Diagnosis—What is the Prescription , 2013 .

[16]  G. N. Roberts,et al.  Thruster fault diagnosis and accommodation for open-frame underwater vehicles , 2004 .

[17]  Xiang Li,et al.  Adaptive region tracking control for autonomous underwater vehicle , 2010, 2010 11th International Conference on Control Automation Robotics & Vision.

[18]  Diego Cabrera,et al.  A fuzzy transition based approach for fault severity prediction in helical gearboxes , 2016, Fuzzy Sets Syst..

[19]  Lai-bin Zhang,et al.  Short-term fault prediction of mechanical rotating parts on the basis of fuzzy-grey optimising method , 2007 .

[20]  Mingjun Zhang,et al.  Multi-fault diagnosis for autonomous underwater vehicle based on fuzzy weighted support vector domain description , 2014 .

[21]  Cheng Xiangqin,et al.  Reliability Research of UUV Based on Fault Tree , 2011 .

[22]  Zhu Daqi.  A Sensor Fault Diagnosis Method for Underwater Vehicles Based on GM(1,1) , 2011 .

[23]  Simon X. Yang,et al.  Unmanned Underwater Vehicles Fault Identification and Fault-Tolerant Control Method Based on FCA-CMAC Neural Networks, Applied on an Actuated Vehicle , 2012, J. Intell. Robotic Syst..

[24]  Mingjun Zhang,et al.  Adaptive sliding mode control based on local recurrent neural networks for underwater robot , 2012 .

[25]  Bo Zhao,et al.  Particle Filter for Fault Diagnosis and Robust Navigation of Underwater Robot , 2014, IEEE Transactions on Control Systems Technology.

[26]  Guangyuan Fu,et al.  Data-driven fault prediction and anomaly measurement for complex systems using support vector probability density estimation , 2018, Eng. Appl. Artif. Intell..

[27]  Sarangapani Jagannathan,et al.  A Novel Fault Diagnostics and Prediction Scheme Using a Nonlinear Observer With Artificial Immune System as an Online Approximator , 2013, IEEE Transactions on Control Systems Technology.

[28]  Gao Shang.  Improvement of GM (1, 1) model , 2007, 2007 IEEE International Conference on Grey Systems and Intelligent Services.