Fault degree identification method for thruster of autonomous underwater vehicle using homomorphic membership function and low frequency trend prediction

Abstract

This article presents a novel thruster fault degree identification method for autonomous underwater vehicle. The novel method is developed from the fuzzy support vector domain description method, which establishes a fault identification model first, and then estimates fault degree according to the model. When establishing fault identification model for thruster based on fuzzy support vector domain description method, it is found that the relative fitting error of the model to the actual fault degree is large, making the model accuracy poor. To reduce the relative fitting error, a homomorphic membership function method is proposed. Different from fuzzy support vector domain description method, which calculates the fuzzy membership degree of fault sample in time domain, the proposed method calculates the fuzzy membership degree in log domain. On estimating thruster fault degree by fuzzy support vector domain description method, it is obtained that the estimated fault degree lags behind the actual fault degree. To shorten the lag time, a low frequency trend prediction method is proposed. Different from fuzzy support vector domain description method, which brings the fault feature extracted from the current surge speed and control voltage into the fault identification model to calculate fault degree, the proposed method firstly forward predicts surge speed and control voltage, and then takes the fault feature extracted from the predicted surge speed and control voltage into the model to acquire fault degree. The effectiveness of the proposed methods is verified by pool experiments of the experimental prototype.

Keywords

Autonomous underwater vehicle thruster fault fault degree identification fuzzy support vector domain description homomorphic membership function low frequency trend prediction

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References

Dearden

Ernits

. Automated fault diagnosis for an autonomous underwater vehicle. IEEE J Ocean Eng 2013; 38: 484–499.

Ismail

Faudzi

Dunnigan

. Fault-tolerant region-based control of an underwater vehicle with kinematically redundant thrusters. Math Probl Eng 2014; 2014: 1–12.

Omerdic

Roberts

. Thruster fault diagnosis and accommodation for open-frame underwater vehicles. Control Eng Pract 2004; 12: 1575–1598.

Liu

Zhu

Yang

. Unmanned underwater vehicles fault identification and fault-tolerant control method based on FCA-CMAC neural networks, applied on an actuated vehicle. J Intell Robot Syst 2012; 66: 463–475.

Lei

Zuo

et al.

A multidimensional hybrid intelligent method for gear fault diagnosis. Expert Syst Appl 2010; 37: 1419–1430.

Yan

Jiang

et al.

A new data mining approach for gear crack level identification based on manifold learning. Mechanics 2012; 18: 29–34.

Wang

Chen

. Performance degradation assessment of rolling bearing based on based on bispectrum and support vector data description. J Vibr Control 2014; 20: 2032–2041.

Yang

Heo

. Abnormality detection via SVDD technique of motor-generator system in HEV. Int J Autom Technol 2014; 15: 637–643.

Cheng

Zhang

. Crack level estimation approach for planetary gearbox based on simulation signal and GRA. J Sound Vibr 2012; 331: 5853–5863.

10.

Zhang

et al.

Gear fault diagnosis method using ensemble empirical mode decomposition energy distribution and grey similar incidence. J Mech Eng 2014; 50: 70–77.

11.

Zeng

Qiu

Liu

et al.

Research on equipment degradation state recognition and fault prognostics method based on KPCA-hidden semi-Markov model. Chin J Sci Instrum 2009; 30: 1341–1346.

12.

Lei

Zuo

. Gear crack level identification based on weighted K nearest neighbor classification algorithm. Mech Syst Signal Pr 2009; 23: 1535–1547.

13.

et al.

Incipient fault intelligent monitoring and diagnosis based on fuzzy support vector data description. J Mech Eng 2005; 41: 145–150.

14.

Shen

Chen

et al.

A monotonic degradation assessment index of rolling bearings using fuzzy support vector data description and running time. Sensors 2012; 12: 10109–10135.

15.

Zhang

Yin

Liu

et al.

Thruster fault identification method for autonomous underwater vehicle using peak region energy and least square grey relational grade. Adv Mech Eng 2015; 7: 1–11.

16.

Zhang

Wang

et al.

Thruster fault diagnosis in autonomous underwater vehicle based on grey qualitative simulation. Ocean Eng 2015; 105: 247–255.

17.

Tax

DMJ

Duin

RPW

. Batch process monitoring based on functional data analysis and support vector data description. J Process Control 2014; 24: 1085–1097.

18.

Zhao

Skjetne

Blanke

et al.

Particle filter for fault diagnosis and robust navigation of underwater robot. IEEE Trans Control Syst Technol 2014; 22: 2399–2407.

19.

Wan

Yang

. Actuator fault diagnosis of automatic underwater vehicle using Gaussian particle filter. J Shanghai Jiaotong Univ 2013; 47: 1702–1706.

20.

Zhang

Yin

Liu

et al.

Feature extraction and fusion for thruster faults of AUV with random disturbance. J Huazhong Univ Sci Technol Nat Sci Ed 2015; 43: 22–26.

21.

Fan

. Wavelets analysis, Wuhan: Wuhan University Press, 2008, pp. 2–2.

22.

Zhang

Wang

. A method of multi-sensor simultaneous fault detection for autonomous underwater vehicle. Robot 2010; 32: 298–305.

23.

Corradini

Monteriù

Orlando

. An actuator failure tolerant control scheme for an underwater remotely operated vehicle. IEEE Transa Control Syst Technol 2011; 19: 1036–1046.

24.

Shen

Chen

et al.

Remaining life predictions of rolling bearing based on relative features and multivariable support vector machine. J Mech Eng 2013; 49: 183–189.