Fault diagnosis method for AUV rudders based on neural network optimized by ant colony algorithm

doi:10.3778/j.issn.1002-8331.2009.19.069

Computer Engineering and Applications ›› 2009, Vol. 45 ›› Issue (19): 224-227.DOI: 10.3778/j.issn.1002-8331.2009.19.069

• 工程与应用 • Previous Articles Next Articles

Fault diagnosis method for AUV rudders based on neural network optimized by ant colony algorithm

YANG Li-ping¹,ZHANG Ming-jun¹,JI Dong-jun²,WANG Yu-jia¹

1.College of Mechanical Electrical Engineering，Harbin Engineering University，Harbin 150001，China
2.Harbin Turbin Co.Ltd，Harbin 150001，China

Received:2009-01-13 Revised:2009-04-03 Online:2009-07-01 Published:2009-07-01
Contact: YANG Li-ping

基于蚁群优化神经网络的AUV舵的故障诊断

杨立平¹,张铭钧¹,季东军²,王玉甲¹

1.哈尔滨工程大学机电工程学院，哈尔滨 150001
2.哈尔滨汽轮机厂有限责任公司，哈尔滨 150001

通讯作者: 杨立平

Abstract

Abstract: Aiming at faults of rudders of streamline Autonomous Underwater Vehicles（AUV），a fault diagnosis method based on the Improved Elman Neural Network（IENN） is presented.Based on the IENN optimized by Ant Colony Algorithm（ACA），the angular speed model of AUV is established.Through the contrastive analysis of the IENN’s training program using ACA and decreasing gradient algorithm respectively，it is testified that the IENN optimized by ACA has the advantages of fast training，global convergence and etc.A fault diagnosis method is presented，which detects rudder’s faults firstly by angular speed residuals and then identifies fault type by an active fault diagnosis method.A rudder fault decision-making method based on the angular speed and the change trend of angular residuals is discussed.In pool environment，the faults of distorted rudders and locked rudders have been simulated.The experiment’s results show that the proposed fault diagnosis method is effective.

Key words: Autonomous Underwater Vehicles（AUV）, Improved Elman Neural Network（IENN）, fault diagnosis

摘要： 针对流线型AUV舵故障，提出了基于Elman神经网络的故障诊断方法。基于蚁群算法优化改进型Elman神经网络，建立了AUV角速度运动模型，通过蚁群算法和梯度下降法对改进型Elman神经网络训练的对比分析，验证了蚁群算法优化的改进型Elman神经网络具有训练速度快，不易陷入最优解等特点。提出了基于角速度残差检测舵故障，再通过定角度航行和定速直航的主动诊断方式，判定舵故障类型的故障诊断方法，探讨了基于角速度残差和角度残差的变化趋势来诊断舵卡死和舵变形故障的故障决策方法。对流线型AUV的舵变形及舵卡死故障进行了水池模拟实验，实验结果验证了所提方法的有效性。

关键词: 自主式水下机器人, 改进型Elman神经网络, 故障诊断

YANG Li-ping¹,ZHANG Ming-jun¹,JI Dong-jun²,WANG Yu-jia¹

. Fault diagnosis method for AUV rudders based on neural network optimized by ant colony algorithm[J]. Computer Engineering and Applications, 2009, 45(19): 224-227.

杨立平¹,张铭钧¹,季东军²,王玉甲¹. 基于蚁群优化神经网络的AUV舵的故障诊断[J]. 计算机工程与应用, 2009, 45(19): 224-227.

[1]	LIN Ronglai, TANG Bingying, CHEN Ming. Data Augmentation Algorithm for Bearings Faults Diagnosis [J]. Computer Engineering and Applications, 2021, 57(7): 269-278.
[2]	CHEN Mingyue, LIU Sanyang. Application of Adaptive Manifold Learning in Fault Diagnosis [J]. Computer Engineering and Applications, 2021, 57(3): 247-252.
[3]	LI Ya, HOU Yandong, LIU Chang. Adaptive Optimal Fault-Tolerant Control Based on Fault Degree [J]. Computer Engineering and Applications, 2021, 57(23): 295-302.
[4]	QIAN Wenbo, XIONG Jianbin, CEN Jian, WANG Qi, YU Dezheng, WU Runjie. Review of Fault Diagnosis of Building Electrical System [J]. Computer Engineering and Applications, 2021, 57(16): 27-39.
[5]	TANG Dengping, CAI Wenjia, ZOU Li, HU Xiang, DING Li, WANG Xue. Application of Improved Convolution Neural Network in Fault Diagnosis of Transformer [J]. Computer Engineering and Applications, 2021, 57(11): 239-247.
[6]	ZHONG Lusheng, LIU Dongdong. Research on Bearing Fault Diagnosis of Multi-level Neural Network [J]. Computer Engineering and Applications, 2020, 56(7): 193-199.
[7]	DU Xiaolei, CHEN Zhigang, XU Xu, ZHANG Nan. Method of Improved Deep Wavelet Auto-Encoder in Bearing Fault Diagnosis [J]. Computer Engineering and Applications, 2020, 56(5): 263-269.
[8]	YU Ping, CAO Jie. Deep Learning Approach and Its Application in Fault Diagnosis and Prognosis [J]. Computer Engineering and Applications, 2020, 56(3): 1-18.
[9]	MAO Biao, YANG Song, LI Yingshun. Improved Dynamic Causality Diagram and Fuzzy Inference Fusion Fault Diagnosis Method [J]. Computer Engineering and Applications, 2020, 56(3): 259-265.
[10]	TANG Wei, ZHENG Yuan, PAN Hong, XU Jingjun. Roller Bearing Fault Diagnosis Based on Stacked Auto-encoder with Dynamic Learning Rate [J]. Computer Engineering and Applications, 2020, 56(20): 264-269.
[11]	WANG Hui, LI Yingshun. Dynamic Fault Diagnosis Algorithm for Improved Evidence Update Rules [J]. Computer Engineering and Applications, 2020, 56(2): 261-265.
[12]	CAO Jie, ZHAO Weiji, YU Ping, WANG Jinhua. Fault Diagnosis of Doubly-fed Generator Based on Improved Bird Swarm Algorithm Optimization PF [J]. Computer Engineering and Applications, 2020, 56(18): 229-237.
[13]	YANG Yu，ZENG Guohui，HUANG Bo. Fault Diagnosis Method of Bearings Based on Dual-Tree Complex Wavelet Packet Transform and Improved SVM [J]. Computer Engineering and Applications, 2020, 56(17): 231-235.
[14]	WANG Dexue, LIN Yi, CHEN Junjie. Application of Cooperative Training Algorithm in Fault Diagnosis of Rolling Bearing [J]. Computer Engineering and Applications, 2020, 56(12): 273-278.
[15]	JIA Baohui, HUANG Lin, LI Yaohua, LIN Yueguo. Performance Optimization Method of FastICA Algorithm for Bearing Fault Diagnosis [J]. Computer Engineering and Applications, 2019, 55(8): 208-214.

Fault diagnosis method for AUV rudders based on neural network optimized by ant colony algorithm

基于蚁群优化神经网络的AUV舵的故障诊断

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics