scholarly journals Actuator Weak Fault Diagnosis in Autonomous Underwater Vehicle Based on Tri-Stable Stochastic Resonance

2020 ◽  
Vol 10 (6) ◽  
pp. 2048 ◽  
Author(s):  
Yang Jiang ◽  
Bo He ◽  
Jia Guo ◽  
Pengfei Lv ◽  
Xiaokai Mu ◽  
...  

The autonomous underwater vehicle (AUV) is mainly used in the development and exploration of the ocean. As an important module of the AUV, the actuator plays an important role in the normal execution of the AUV. Therefore, the fault diagnosis of the actuator is particularly important. At present, the research on the strong faults, such as the winding of the actuator, has achieved good results, but the research on the weak fault diagnosis is relatively rare. In this paper, the tri-stable stochastic resonance model is analyzed, and the ant colony tri-stable stochastic resonance model is used to diagnose the weak fault. The system accurately diagnoses the fault of the actuator collision and verifies the adaptive tri-stable stochastic resonance system. This model has better diagnostic results than the bi-stable stochastic resonance system.

2015 ◽  
Vol 105 ◽  
pp. 247-255 ◽  
Author(s):  
Ming-jun Zhang ◽  
Yu-jia Wang ◽  
Jian-an Xu ◽  
Zheng-chen Liu

2019 ◽  
Vol 26 (7) ◽  
pp. 1910-1920 ◽  
Author(s):  
Jin-tian Yin ◽  
Yong-fang Xie ◽  
Zhi-wen Chen ◽  
Tao Peng ◽  
Chun-hua Yang

Data in Brief ◽  
2021 ◽  
pp. 107477
Author(s):  
Daxiong Ji ◽  
Xin Yao ◽  
Shuo Li ◽  
Yuangui Tang ◽  
Yu Tian

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Peiming Shi ◽  
Cuijiao Su ◽  
Dongying Han

An adaptive stochastic resonance and analytical mode decomposition-ensemble empirical mode decomposition (AMD-EEMD) method is proposed for fault diagnosis of rotating machinery in this paper. Firstly, the stochastic resonance system is optimized by particle swarm optimization (PSO), and the best structure parameters are obtained. Then, the signal with noise is put into the stochastic resonance system and denoising and enhancing the signal. Secondly, the signal output from the stochastic resonance system is extracted by analytical mode decomposition (AMD) method. Finally, the signal is decomposed by ensemble empirical mode decomposition (EEMD) method. The simulation results show that the optimal stochastic resonance system can effectively improve the signal-to-noise ratio, and the number of effective components of EEMD decomposition is significantly reduced after using AMD, thus improving the decomposition results of EEMD and enhancing the amplitude of components frequency. Through the extraction of the rolling bearing fault signal feature proved that the method has a good effect.


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