Weak thruster fault detection for AUV based on stochastic resonance and wavelet reconstruction

When the bi-stable stochastic resonance method was applied to enhance weak thruster fault for autonomous underwater vehicle (AUV), the enhancement performance could not satisfy the detection requirement of weak thruster fault. As for this problem, a fault feature enhancement method based on mono-stable stochastic resonance was proposed. In the method, in order to improve the enhancement performance of weak thruster fault feature, the conventional bi-stable potential function was changed to mono-stable potential function which was more suitable for aperiodic signals. Furthermore, when particle swarm optimization was adopted to adjust the parameters of mono-stable stochastic resonance system, the global convergent time would be long. An improved particle swarm optimization method was developed by changing the linear inertial weighted function as nonlinear function with cosine function, so as to reduce the global convergent time. In addition, when the conventional wavelet reconstruction method was adopted to detect the weak thruster fault, undetected fault or false alarm may occur. In order to successfully detect the weak thruster fault, a weak thruster detection method was proposed based on the integration of stochastic resonance and wavelet reconstruction. In the method, the optimal reconstruction scale was determined by comparing wavelet entropies corresponding to each decomposition scale. Finally, pool-experiments were performed on AUV with thruster fault. The effectiveness of the proposed mono-stable stochastic resonance method in enhancing fault feature and reducing the global convergent time was demonstrated in comparison with particle swarm optimization based bi-stochastic resonance method. Furthermore, the effectiveness of the proposed fault detection method was illustrated in comparison with the conventional wavelet reconstruction.

[1]  Mingjun Zhang,et al.  A Method of Multi-sensor Simultaneous Faults Detection for Autonomous Underwater Vehicle: A Method of Multi-sensor Simultaneous Faults Detection for Autonomous Underwater Vehicle , 2010 .

[2]  Zheng Qin,et al.  Sensor fault detection and identification based on gray model for autonomous underwater vehicle , 2009 .

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

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

[5]  Zhengjia He,et al.  Adaptive stochastic resonance method for impact signal detection based on sliding window , 2013 .

[6]  L. Qiang,et al.  Engineering signal processing based on adaptive step-changed stochastic resonance , 2007 .

[7]  Yongjie Pang,et al.  Adaptive output feedback control based on DRFNN for AUV , 2009 .

[8]  Yang Bin.  Alternately preying particle swarm optimization algorithm and convergence analysis of its particle trajectories , 2013 .

[9]  Wang Yujia,et al.  A Method of Multi-sensor Simultaneous Faults Detection for Autonomous Underwater Vehicle , 2010 .

[10]  Shangbin Jiao,et al.  Multi-frequency Weak Signal Detection Method Based on Adaptive Stochastic Resonance with Knowledge-based PSO , 2014 .

[11]  Gaigai Cai,et al.  Transient signal analysis based on Levenberg–Marquardt method for fault feature extraction of rotating machines , 2015 .

[12]  Tomasz Barszcz,et al.  A novel method for the optimal band selection for vibration signal demodulation and comparison with the Kurtogram , 2011 .

[13]  Xiang Zhang,et al.  Nonlinear trajectory tracking control of a new autonomous underwater vehicle in complex sea conditions , 2012, Journal of Central South University.

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

[15]  Adam P. Piotrowski,et al.  How novel is the "novel" black hole optimization approach? , 2014, Inf. Sci..

[16]  Guobiao Wang,et al.  Basic Research on Machinery Fault Diagnosis—What is the Prescription , 2013 .

[17]  Yi Qin,et al.  Adaptive bistable stochastic resonance and its application in mechanical fault feature extraction , 2014 .

[18]  Guo Yan,et al.  Study of the property of the parameters of bistable stochastic resonance , 2007 .

[19]  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..

[20]  Tang Wanwen,et al.  Fault Diagnosis of UV's Sensors Based on Wavelet Neural Network , 2010 .

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

[22]  Dong Han,et al.  Planetary gearbox fault diagnosis using an adaptive stochastic resonance method , 2013 .

[23]  David M. Lane,et al.  An integrated diagnostic architecture for autonomous underwater vehicles , 2007, J. Field Robotics.

[24]  Wenyi Liu,et al.  Wind turbine fault diagnosis based on Morlet wavelet transformation and Wigner-Ville distribution , 2010 .

[25]  Wang Li-rong.  Sensor Fault Diagnosis of Autonomous Underwater Vehicle , 2006 .

[26]  Zhi-wen Yang,et al.  An improved coupling of numerical and physical models for simulating wave propagation , 2014 .