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              基于深層卷積隨機配置網絡的電熔鎂爐工況識別方法研究

              李帷韜 童倩倩 王殿輝 吳高昌

              李帷韜, 童倩倩, 王殿輝, 吳高昌. 基于深層卷積隨機配置網絡的電熔鎂爐工況識別方法研究. 自動化學報, 2024, 50(3): 527?543 doi: 10.16383/j.aas.c230272
              引用本文: 李帷韜, 童倩倩, 王殿輝, 吳高昌. 基于深層卷積隨機配置網絡的電熔鎂爐工況識別方法研究. 自動化學報, 2024, 50(3): 527?543 doi: 10.16383/j.aas.c230272
              Li Wei-Tao, Tong Qian-Qian, Wang Dian-Hui, Wu Gao-Chang. Research on fused magnesium furnace working condition recognition method based on deep convolutional stochastic configuration networks. Acta Automatica Sinica, 2024, 50(3): 527?543 doi: 10.16383/j.aas.c230272
              Citation: Li Wei-Tao, Tong Qian-Qian, Wang Dian-Hui, Wu Gao-Chang. Research on fused magnesium furnace working condition recognition method based on deep convolutional stochastic configuration networks. Acta Automatica Sinica, 2024, 50(3): 527?543 doi: 10.16383/j.aas.c230272

              基于深層卷積隨機配置網絡的電熔鎂爐工況識別方法研究

              doi: 10.16383/j.aas.c230272
              基金項目: 國家重點研發計劃(2018AAA0100304), 國家自然科學基金(62173120, 62103092), 安徽省自然科學基金(2108085UD11), 111引智項目(BP0719039)資助
              詳細信息
                作者簡介:

                李帷韜:合肥工業大學電氣與自動化工程學院副教授. 主要研究方向為深度學習, 圖像處理和智能認知. E-mail: wtli@hfut.edu.cn

                童倩倩:合肥工業大學電氣與自動化工程學院碩士研究生. 主要研究方向為智能認知. E-mail: 2021110400@mail.hfut.edu.cn

                王殿輝:中國礦業大學人工智能研究院教授. 主要研究方向為工業大數據建模與分析, 隨機配置學習理論及工業應用. 本文通信作者. E-mail: dh.wang@deepscn.com

                吳高昌:東北大學流程工業綜合自動化國家重點實驗室副教授. 主要研究方向為智能計算成像, 深度學習和異常工況智能感知與預測. E-mail: wugc@mail.neu.edu.cn

              Research on Fused Magnesium Furnace Working Condition Recognition Method Based on Deep Convolutional Stochastic Configuration Networks

              Funds: Supported by National Key Research and Development Program of China (2018AAA0100304), National Natural Science Foundation of China (62173120, 62103092), Anhui Provincial Natural Science Foundation (2108085UD11), and 111 Project (BP0719039)
              More Information
                Author Bio:

                LI Wei-Tao Associate professor at the School of Electrical Engineering and Automation, Hefei University of Technology. His research interest covers deep learning, image processing, and intelligent cognition

                TONG Qian-Qian Master student at the School of Electrical Engineering and Automation, Hefei University of Technology. Her main research interest is intelligent cognition

                WANG Dian-Hui Professor at the Institute of Artificial Intelligence, China University of Mining and Technology. His research interest covers industrial big data modeling and analysis, stochastic configuration learning theory and industrial applications. Corresponding author of this paper

                WU Gao-Chang Associate professor at the State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University. His research interest covers intelligent computational imaging, deep learning, and intelligent sensing and prediction of abnormal working conditions

              • 摘要: 為解決電熔鎂爐工況識別模型泛化能力和可解釋性弱的缺陷, 提出一種基于深層卷積隨機配置網絡(Deep convolutional stochastic configuration networks, DCSCN)的可解釋性電熔鎂爐異常工況識別方法. 首先, 基于監督學習機制生成具有物理含義的高斯差分卷積核, 采用增量式方法構建深層卷積神經網絡(Deep convolutional neural network, DCNN), 確保識別誤差逐級收斂, 避免反向傳播算法迭代尋優卷積核參數的過程. 定義通道特征圖獨立系數獲取電熔鎂爐特征類激活映射圖的可視化結果, 定義可解釋性可信度評測指標, 自適應調節深層卷積隨機配置網絡層級, 對不可信樣本進行再認知以獲取最優工況識別結果. 實驗結果表明, 所提方法較其他方法具有更優的識別精度和可解釋性.
              • 圖  1  基于深層卷積隨機配置網絡的可解釋電熔鎂爐工況識別模型結構圖

                Fig.  1  Structure of interpretable fused magnesium furnace working condition recognition model based on deep convolutional stochastic configuration networks

                圖  2  深層卷積隨機配置網絡結構圖

                Fig.  2  Deep convolutional stochastic configuration networks structure diagram

                圖  3  基于特征圖獨立性得分的類激活映射示意圖

                Fig.  3  Schematic diagram of the class activation mapping based on feature map independence scores

                圖  4  正常工況圖像數據增強后的結果

                Fig.  4  Results of normal conditions image data enhancement

                圖  5  欠燒工況圖像數據增強后的結果

                Fig.  5  Results after image data enhancement for underburning conditions

                圖  6  過熱工況圖像數據增強后的結果

                Fig.  6  Results after image data enhancement for superheated operating conditions

                圖  7  異常排氣工況圖像數據增強后的結果

                Fig.  7  Results after image data enhancement for abnormal exhaust conditions

                圖  8  不同卷積核大小條件下的識別精度曲線

                Fig.  8  Recognition accuracy curves under different convolutional kernel sizes

                圖  9  強化學習訓練過程的平均獎勵曲線

                Fig.  9  Average reward curves for training process of reinforcement learning methods

                圖  10  不同卷積層類激活映射圖

                Fig.  10  Different convolutional layer class activation mapping maps

                圖  11  本文方法與基于強化學習的類激活映射圖對比

                Fig.  11  Comparison of the method proposed in this paper with the class activation mapping maps based on reinforcement learning

                圖  12  本文方法與基于強化學習的可信識別樣本比例變化曲線

                Fig.  12  The proportion change curves of trusted recognition samples based on reinforcement learning and the method proposed in this paper

                圖  13  不同網絡模型的訓練樣本識別精度曲線

                Fig.  13  Recognition accuracy curves of training samples for different network models

                表  1  基于強化學習的漏診率、誤診率和精度對比 (%)

                Table  1  Comparison of missed diagnosis rate, misdiagnosis rate and accuracy based on reinforcement learning (%)

                模型訓練集測試集
                漏診率誤診率精度漏診率誤診率精度
                單層本文方法7.61 ± 0.1899.15 ± 0.33183.24 ± 0.1959.95 ± 0.216 10.30 ± 0.231 79.75 ± 0.108
                強化學習9.08 ± 0.08210.14 ± 0.35480.76 ± 0.22810.51 ± 0.172 12.81 ± 0.390 76.68 ± 0.305
                三層本文方法5.31 ± 0.2391.96 ± 0.16592.73 ± 0.1665.24 ± 0.2452.45 ± 0.20392.31 ± 0.283
                強化學習7.36 ± 0.3612.58 ± 0.31390.06 ± 0.3136.57 ± 0.361 3.61 ± 0.31389.82 ± 0.329
                下載: 導出CSV

                表  2  消融實驗結果 (%)

                Table  2  Results of ablation experiments (%)

                模型訓練集測試集
                漏診率誤診率精度漏診率誤診率精度
                本文方法5.31 ± 0.2391.96 ± 0.16592.73 ± 0.1665.24 ± 0.2452.45 ± 0.20392.31 ± 0.283
                未加入可解釋性模塊5.57 ± 0.2322.51 ± 0.22391.92 ± 0.2787.29 ± 0.1731.59 ± 0.18191.12 ± 0.347
                未加入高斯卷積核4.29 ± 0.2744.51 ± 0.39191.20 ± 0.2643.45 ± 0.2552.50 ± 0.32990.54 ± 0.231
                未加入可解釋性模塊以及高斯卷積核6.02 ± 0.1834.25 ± 0.23189.73 ± 0.3254.13 ± 0.2426.73 ± 0.22889.14 ± 0.179
                下載: 導出CSV

                表  3  不同高斯噪聲的實驗結果 (%)

                Table  3  Experimental results with different Gaussian noises (%)

                模型訓練集測試集
                漏診率誤診率精度漏診率誤診率精度
                本文方法($\eta=0.3$)5.31 ± 0.2391.96 ± 0.16592.73 ± 0.1665.24 ± 0.2452.45 ± 0.20392.31 ± 0.283
                $\eta=0.6$模型6.92 ± 0.2322.21 ± 0.22390.87 ± 0.2067.19 ± 0.1732.52 ± 0.18190.29 ± 0.347
                $\eta=0.9$模型8.31 ± 0.4232.29 ± 0.24889.40 ± 0.2977.45 ± 0.3827.01 ± 0.27485.54 ± 0.288
                下載: 導出CSV

                表  4  不同模型的測試樣本漏診率、誤診率和精度對比 (%)

                Table  4  Comparison of missed diagnosis rate, misdiagnosis rate and accuracy of test samples with different models (%)

                模型漏診率誤診率精度
                SCN14.21 ± 0.22814.21 ± 0.22876.14 ± 0.215
                塊增量BSC12.58 ± 0.28510.57 ± 0.15376.85 ± 0.233
                2DSCN6.49 ± 0.26315.52 ± 0.30377.99 ± 0.353
                DeepSCN9.04 ± 0.2857.32 ± 0.07583.64 ± 0.209
                CNN6.82 ± 0.3765.46 ± 0.16787.72 ± 0.231
                貝葉斯網絡[6]5.36 ± 0.2684.72 ± 0.252 89.92 ± 0.256
                CNN+LSTM[8]6.91 ± 0.2013.52 ± 0.18489.57 ± 0.337
                本文方法5.24 ± 0.2452.45 ± 0.20392.31 ± 0.283
                下載: 導出CSV

                表  5  不同識別模型的綜合性能對比

                Table  5  Comprehensive performance comparison of different recognition models

                模型參數量(MB)訓練時間(s)測試時間(s)
                SCN500.03810278.8340.011
                塊增量BSC500.0388341.0940.011
                2DSCN1000.03812352.7710.013
                DeepSCN127.89915411.0810.013
                CNN0.66420714.3220.014
                貝葉斯網絡[6]0.04626.2580.022
                CNN+LSTM[8]4.12720159.6420.015
                本文方法12.85418218.0210.014
                下載: 導出CSV

                表  6  太陽能電池板數據集實驗結果對比 (%)

                Table  6  Comparison of experimental results for solar panel dataset (%)

                模型漏診率誤診率精度
                單層本文方法7.31$\pm$0.1877.86$\pm$0.25984.83$\pm$0.245
                未加入可解釋性模塊9.87$\pm$0.2526.94$\pm$0.24383.19$\pm$0.279
                三層本文方法3.45$\pm$0.2133.51$\pm$0.16993.04$\pm$0.323
                未加入可解釋性模塊4.13$\pm$0.1924.22$\pm$0.25791.65$\pm$0.236
                下載: 導出CSV
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                        出版歷程
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                        • 錄用日期:  2023-09-26
                        • 網絡出版日期:  2024-02-27
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