针对工程实际故障诊断环境下,可用数据稀缺,导致智能诊断模型对轴承健康状态识别精度较低这一问题,提出一种基于二次迁移学习和EfficientNetV2(Two-Step Transfer of Efficient⁃NetV2,TSTE)的滚动轴承故障诊断新方法。首先,将模型在轴...针对工程实际故障诊断环境下,可用数据稀缺,导致智能诊断模型对轴承健康状态识别精度较低这一问题,提出一种基于二次迁移学习和EfficientNetV2(Two-Step Transfer of Efficient⁃NetV2,TSTE)的滚动轴承故障诊断新方法。首先,将模型在轴承全寿命周期数据集中训练,之后冻结模型浅层权重,将其在多工况轴承数据集中训练,进行第一次迁移学习。其次,通过构造类不平衡数据集,研究实际故障环境下可用数据稀缺对故障诊断性能的影响。然后,基于合成少数类过采样技术(Synthetic Minority Oversampling Technique,SMOTE)过采样方法与编辑最近邻(Edited Nearest Neighbors,ENN)欠采样方法对故障数据进行扩充,使类不平衡数据集重构为类平衡数据集。最后,将模型在类平衡数据集中训练,冻结模型底层权重,训练模型深层,进行第二次迁移学习,使模型掌握平衡数据集故障特征。通过多种指标进行实验评估,同时与其他方法进行对比,并使用Grad-CAM方法进行了特征可视化。结果表明,所提方法能够将模型在实验室环境下积累的故障诊断知识应用于实际工程设备,适用于检测数据稀缺情形下的滚动轴承故障诊断。展开更多
Plant disease classification and prevention of spreading of the disease at earlier stages based on visual leaves symptoms and Pest recognition through deep learning-based image classification is in the forefront of re...Plant disease classification and prevention of spreading of the disease at earlier stages based on visual leaves symptoms and Pest recognition through deep learning-based image classification is in the forefront of research.To perform the investigation on Plant and pest classification,Transfer Learning(TL)approach is used on EfficientNet-V2.TL requires limited labelled data and shorter training time.However,the limitation of TL is the pre-trained model network’s topology is static and the knowledge acquired is detrimentally overwriting the old parameters.EfficientNet-V2 is a Convolutional Neural Network(CNN)model with significant high speed learning rates across variable sized datasets.The model employs a form of progressive learning mechanism which expands the network topology gradually over the course of training process improving the model’s learning capacity.This provides a better interpretability of the model’s understanding on the test domains.With these insights,our work investigates the effectiveness of EfficienetV2 model trained on a class imbalanced dataset for plant disease classification and pest recognition by means of combining TL and progressive learning approach.This Progressive Learning for TL(PL-TL)is used in our work consisting of 38 classes of PlantVillage dataset of crops and fruit species,5 classes of cassava leaf diseases and another dataset with around 102 classes of crop pest images downloaded from popular dataset platforms,though it is not a benchmark dataset.To test the predictability rate of the model in classifying leaf diseases with similar visual symptoms,Mix-up data augmentation technique is used at the ratio of 1:4 on corn and tomato classes which has high probability of misinterpretation of disease classes.Also,the paper compares the TL approach performed on the above mentioned three types of data set using well established CNN based Inceptionv3,and Vision Transformer a non-CNN model.It clearly depicts that EfficientNetV2 has an outstanding performance of 99.5%,97.5%,80.1%on Cassava,PlantVillage and IP102 datasets respectively at a faster rate irrespective of the data size and class distribution as compared to Inception-V3 and ViT models.The performance metrics in terms of accuracy,precision,f1-score is also studied.展开更多
文摘针对工程实际故障诊断环境下,可用数据稀缺,导致智能诊断模型对轴承健康状态识别精度较低这一问题,提出一种基于二次迁移学习和EfficientNetV2(Two-Step Transfer of Efficient⁃NetV2,TSTE)的滚动轴承故障诊断新方法。首先,将模型在轴承全寿命周期数据集中训练,之后冻结模型浅层权重,将其在多工况轴承数据集中训练,进行第一次迁移学习。其次,通过构造类不平衡数据集,研究实际故障环境下可用数据稀缺对故障诊断性能的影响。然后,基于合成少数类过采样技术(Synthetic Minority Oversampling Technique,SMOTE)过采样方法与编辑最近邻(Edited Nearest Neighbors,ENN)欠采样方法对故障数据进行扩充,使类不平衡数据集重构为类平衡数据集。最后,将模型在类平衡数据集中训练,冻结模型底层权重,训练模型深层,进行第二次迁移学习,使模型掌握平衡数据集故障特征。通过多种指标进行实验评估,同时与其他方法进行对比,并使用Grad-CAM方法进行了特征可视化。结果表明,所提方法能够将模型在实验室环境下积累的故障诊断知识应用于实际工程设备,适用于检测数据稀缺情形下的滚动轴承故障诊断。
文摘Plant disease classification and prevention of spreading of the disease at earlier stages based on visual leaves symptoms and Pest recognition through deep learning-based image classification is in the forefront of research.To perform the investigation on Plant and pest classification,Transfer Learning(TL)approach is used on EfficientNet-V2.TL requires limited labelled data and shorter training time.However,the limitation of TL is the pre-trained model network’s topology is static and the knowledge acquired is detrimentally overwriting the old parameters.EfficientNet-V2 is a Convolutional Neural Network(CNN)model with significant high speed learning rates across variable sized datasets.The model employs a form of progressive learning mechanism which expands the network topology gradually over the course of training process improving the model’s learning capacity.This provides a better interpretability of the model’s understanding on the test domains.With these insights,our work investigates the effectiveness of EfficienetV2 model trained on a class imbalanced dataset for plant disease classification and pest recognition by means of combining TL and progressive learning approach.This Progressive Learning for TL(PL-TL)is used in our work consisting of 38 classes of PlantVillage dataset of crops and fruit species,5 classes of cassava leaf diseases and another dataset with around 102 classes of crop pest images downloaded from popular dataset platforms,though it is not a benchmark dataset.To test the predictability rate of the model in classifying leaf diseases with similar visual symptoms,Mix-up data augmentation technique is used at the ratio of 1:4 on corn and tomato classes which has high probability of misinterpretation of disease classes.Also,the paper compares the TL approach performed on the above mentioned three types of data set using well established CNN based Inceptionv3,and Vision Transformer a non-CNN model.It clearly depicts that EfficientNetV2 has an outstanding performance of 99.5%,97.5%,80.1%on Cassava,PlantVillage and IP102 datasets respectively at a faster rate irrespective of the data size and class distribution as compared to Inception-V3 and ViT models.The performance metrics in terms of accuracy,precision,f1-score is also studied.
