水利学报2023，Vol.54Issue(11)：1380-1391,12.DOI:10.13243/j.cnki.slxb.20230442
基于增强层次对称点图像分析和深度残差网络的水电机组故障诊断
Fault diagnosis of hydropower units based on enhanced hierarchical Symmetrized Dot Pattern and Resnet50
摘要
Abstract
Image transformation has certain potential in the field of fault diagnosis of hydropower units.The tradi-tional methods of transforming one-dimensional data into images has the problems,such as image feature singulari-ty,the difficulty of representing multiple signals with one image,and the low accuracy of image recognition.Therefore,this paper proposes a fault diagnosis method based on enhanced hierarchical symmetrized dot pattern(EHSDP)and deep residual network(Resnet50).Firstly,instead of the traditional hierarchical decomposition,the image transformation method of EHSDP is proposed by utilizing the moving difference and moving average process,which can overcome the problem of single signal feature while improving the image transformation efficien-cy by 27.42％.Secondly,the decomposed vibration signals are imaged to obtain the image database of the hydro-power units,and the EHSDP images are divided into a training set and a validation set.The Resnet50 model is trained with the training set to obtain the optimal model parameters.Then,the validation set images are input into the trained Resnet50,and the extracted features are visualized with TSNE.There is no aliasing in the characteristic signals of each state.Finally,the output image feature classification realized the fault diagnosis of hydropower units and the real machine data of SK-3# units of a hydropower plant was used for validation.The results of both simula-tion experiments and example verification show that the proposed method has obvious advantages in all the compari-son models,which verifies the effectiveness and practicality of the proposed method in this paper.关键词
振动信号/增强层次/图像识别/故障诊断/数据驱动Key words
vibration signal/enhance hierarchy/image recognition/fault diagnosis/data driven分类
信息技术与安全科学引用本文复制引用
张婷婷,王斌,王坤,相里宇锡,陈飞,陈帝伊..基于增强层次对称点图像分析和深度残差网络的水电机组故障诊断[J].水利学报,2023,54(11):1380-1391,12.基金项目
国家自然科学基金项目(51509210) （51509210）
陕西省重点研发计划项目(2021NY-181) （2021NY-181）
