Knowledge Commons of Institute of Automation,CAS
Fault Diagnosis for Robotic Fish Sensors based on Spatial Domain Image Fusion and Convolution Neural Network | |
Xuqing Fan1,2; Sai Deng1,2; Junfeng Fan1,2; Chao Zhou1,2; Zhengxing Wu1,2; Yaming Ou1,2; Bin Zhang1,2 | |
2023 | |
会议名称 | the 42nd Chinese Control Conference |
会议日期 | 2023-7 |
会议地点 | Tianjin, China |
摘要 | The accurate detection of faults in robotic fish allows for improving the safety and reliability of its operations. This paper proposes a depth sensor fault diagnosis method based on Gramian Angular Field Fusion and Convolutional Neural Network (GAFF-CNN). Firstly, the depth sensor signals are augmented by a sliding window with overlapping data. Secondly, the one-dimensional time series sensor signals are converted into two-dimensional images by using Gramian Angular Field (GAF). To improve fault diagnosis accuracy and accelerate the training speed, using a weighted fusion method to fuse Gramian Angular Summation Field (GASF) and Gramian Angular Difference Field (GADF). After that, the model of CNN is established to train and test fused images for fault diagnosis. The result shows that the fault diagnosis accuracy is the highest at 97.22% when using a weighted coefficient of 0.3, and when the weighted coefficient is 0.4, the training speed is the fastest. |
关键词 | Fault Diagnosis GAF Fusion CNN Robotic Fish |
语种 | 英语 |
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 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/57232 |
专题 | 复杂系统认知与决策实验室_水下机器人 |
通讯作者 | Sai Deng |
作者单位 | 1.The Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences 2.University of Chinese Academy of Sciences |
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 | Xuqing Fan,Sai Deng,Junfeng Fan,et al. Fault Diagnosis for Robotic Fish Sensors based on Spatial Domain Image Fusion and Convolution Neural Network[C],2023. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Fault_Diagnosis_for_(1492KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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