Dissolved gas analysis(DGA)is an effective online fault diagnosis technique for large oil-immersed transformers.However,due to the limited number of DGA data,most deep learning models will be overfitted and the classi...Dissolved gas analysis(DGA)is an effective online fault diagnosis technique for large oil-immersed transformers.However,due to the limited number of DGA data,most deep learning models will be overfitted and the classification accuracy cannot be guaranteed.Therefore,this paper has introduced the idea of deep neural networks into the multi-grained cascade forest(gcForest),which is a tree-based deep learning model,and proposed an improved gcForest that can be accelerated by GPU.Firstly,in order to extract features more effectively and reduce memory consumption,the multi-grained scanning of gcForest is replaced by convolutional neural networks.Secondly,the cascade forest(CasForest)is replaced by cascade eXtreme gradient boosting(CasXGBoost)to improve the classification ability.Finally,235 DGA samples are used to train and evaluate the proposed model.The average fault diagnosis accuracy of the improved gcForest is 88.08%,while the average recall,precision,and Fl-score are 0.89,0.90,0.89,respectively.Moreover,the proposed method still has high fault diagnosis accuracy for datasets of different sizes.展开更多
Higher requirements for the accuracy of relevant models are put throughout the transformation and upgrade of the iron and steel sector to intelligent production.It has been difficult to meet the needs of the field wit...Higher requirements for the accuracy of relevant models are put throughout the transformation and upgrade of the iron and steel sector to intelligent production.It has been difficult to meet the needs of the field with the usual prediction model of mechanical properties of hotrolled strip.Insufficient data and difficult parameter adjustment limit deep learning models based on multi-layer networks in practical applications;besides,the limited discrete process parameters used make it impossible to effectively depict the actual strip processing process.In order to solve these problems,this research proposed a new sampling approach for mechanical characteristics input data of hot-rolled strip based on the multi-grained cascade forest(gcForest)framework.According to the characteristics of complex process flow and abnormal sensitivity of process path and parameters to product quality in the hot-rolled strip production,a three-dimensional continuous time series process data sampling method based on time-temperature-deformation was designed.The basic information of strip steel(chemical composition and typical process parameters)is fused with the local process information collected by multi-grained scanning,so that the next link’s input has both local and global features.Furthermore,in the multi-grained scanning structure,a sub sampling scheme with a variable window was designed,so that input data with different dimensions can get output characteristics of the same dimension after passing through the multi-grained scanning structure,allowing the cascade forest structure to be trained normally.Finally,actual production data of three steel grades was used to conduct the experimental evaluation.The results revealed that the gcForest-based mechanical property prediction model outperforms the competition in terms of comprehensive performance,ease of parameter adjustment,and ability to sustain high prediction accuracy with fewer samples.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant(52277138)Natural Science Foundation of Guangxi under Grant(2018JJB160064,2018JJA160176)。
文摘Dissolved gas analysis(DGA)is an effective online fault diagnosis technique for large oil-immersed transformers.However,due to the limited number of DGA data,most deep learning models will be overfitted and the classification accuracy cannot be guaranteed.Therefore,this paper has introduced the idea of deep neural networks into the multi-grained cascade forest(gcForest),which is a tree-based deep learning model,and proposed an improved gcForest that can be accelerated by GPU.Firstly,in order to extract features more effectively and reduce memory consumption,the multi-grained scanning of gcForest is replaced by convolutional neural networks.Secondly,the cascade forest(CasForest)is replaced by cascade eXtreme gradient boosting(CasXGBoost)to improve the classification ability.Finally,235 DGA samples are used to train and evaluate the proposed model.The average fault diagnosis accuracy of the improved gcForest is 88.08%,while the average recall,precision,and Fl-score are 0.89,0.90,0.89,respectively.Moreover,the proposed method still has high fault diagnosis accuracy for datasets of different sizes.
基金financially supported by the National Natural Science Foundation of China(No.52004029)the Fundamental Research Funds for the Central Universities,China(No.FRF-TT-20-06).
文摘Higher requirements for the accuracy of relevant models are put throughout the transformation and upgrade of the iron and steel sector to intelligent production.It has been difficult to meet the needs of the field with the usual prediction model of mechanical properties of hotrolled strip.Insufficient data and difficult parameter adjustment limit deep learning models based on multi-layer networks in practical applications;besides,the limited discrete process parameters used make it impossible to effectively depict the actual strip processing process.In order to solve these problems,this research proposed a new sampling approach for mechanical characteristics input data of hot-rolled strip based on the multi-grained cascade forest(gcForest)framework.According to the characteristics of complex process flow and abnormal sensitivity of process path and parameters to product quality in the hot-rolled strip production,a three-dimensional continuous time series process data sampling method based on time-temperature-deformation was designed.The basic information of strip steel(chemical composition and typical process parameters)is fused with the local process information collected by multi-grained scanning,so that the next link’s input has both local and global features.Furthermore,in the multi-grained scanning structure,a sub sampling scheme with a variable window was designed,so that input data with different dimensions can get output characteristics of the same dimension after passing through the multi-grained scanning structure,allowing the cascade forest structure to be trained normally.Finally,actual production data of three steel grades was used to conduct the experimental evaluation.The results revealed that the gcForest-based mechanical property prediction model outperforms the competition in terms of comprehensive performance,ease of parameter adjustment,and ability to sustain high prediction accuracy with fewer samples.
