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Spatial Domain Image Fusion with Particle Swarm Optimization and Lightweight AlexNet for Robotic Fish Sensor Fault Diagnosis | |
Fan, Xuqing1,2![]() ![]() ![]() ![]() ![]() | |
发表期刊 | BIOMIMETICS
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2023-10-01 | |
卷号 | 8期号:6页码:489 |
产权排序 | 1 |
摘要 | Safety and reliability are vital for robotic fish, which can be improved through fault diagnosis. In this study, a method for diagnosing sensor faults is proposed, which involves using Gramian angular field fusion with particle swarm optimization and lightweight AlexNet. Initially, one-dimensional time series sensor signals are converted into two-dimensional images using the Gramian angular field method with sliding window augmentation. Next, weighted fusion methods are employed to combine Gramian angular summation field images and Gramian angular difference field images, allowing for the full utilization of image information. Subsequently, a lightweight AlexNet is developed to extract features and classify fused images for fault diagnosis with fewer parameters and a shorter running time. To improve diagnosis accuracy, the particle swarm optimization algorithm is used to optimize the weighted fusion coefficient. The results indicate that the proposed method achieves a fault diagnosis accuracy of 99.72% when the weighted fusion coefficient is 0.276. These findings demonstrate the effectiveness of the proposed method for diagnosing depth sensor faults in robotic fish. |
关键词 | image fusion lightweight AlexNet particle swarm optimization fault diagnosis robotic fish |
DOI | 10.3390/biomimetics8060489 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | Youth Innovation Promotion Association of CAS[2023142] ; National Natural Science Foundation of China[61903362] ; National Natural Science Foundation of China[62003341] |
项目资助者 | Youth Innovation Promotion Association of CAS ; National Natural Science Foundation of China |
WOS研究方向 | Engineering ; Materials Science |
WOS类目 | Engineering, Multidisciplinary ; Materials Science, Biomaterials |
WOS记录号 | WOS:001094201400001 |
出版者 | MDPI |
七大方向——子方向分类 | 智能机器人 |
国重实验室规划方向分类 | 水下仿生机器人 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/54428 |
专题 | 复杂系统认知与决策实验室_水下机器人 |
通讯作者 | Deng, Sai |
作者单位 | 1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 101408, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Fan, Xuqing,Deng, Sai,Wu, Zhengxing,et al. Spatial Domain Image Fusion with Particle Swarm Optimization and Lightweight AlexNet for Robotic Fish Sensor Fault Diagnosis[J]. BIOMIMETICS,2023,8(6):489. |
APA | Fan, Xuqing,Deng, Sai,Wu, Zhengxing,Fan, Junfeng,&Zhou, Chao.(2023).Spatial Domain Image Fusion with Particle Swarm Optimization and Lightweight AlexNet for Robotic Fish Sensor Fault Diagnosis.BIOMIMETICS,8(6),489. |
MLA | Fan, Xuqing,et al."Spatial Domain Image Fusion with Particle Swarm Optimization and Lightweight AlexNet for Robotic Fish Sensor Fault Diagnosis".BIOMIMETICS 8.6(2023):489. |
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Spatial Domain Image(5062KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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