基于深層卷積隨機配置網(wǎng)絡(luò )的電熔鎂爐工況識別方法研究
doi: 10.16383/j.aas.c230272
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合肥工業(yè)大學(xué)電氣與自動(dòng)化工程學(xué)院 合肥 230009
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中國礦業(yè)大學(xué)人工智能研究院 徐州 221116
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東北大學(xué)流程工業(yè)綜合自動(dòng)化國家重點(diǎn)實(shí)驗室 沈陽(yáng) 110819
Research on Fused Magnesium Furnace Working Condition Recognition Method Based on Deep Convolutional Stochastic Configuration Networks
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School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009
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Institute of Artificial Intelligence, China University of Mining and Technology, Xuzhou 221116
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State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819
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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(wǎng)絡(luò )(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(xué)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(jīng)網(wǎng)絡(luò )(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(yōu)卷積核參數的過(guò)程. 定義通道特征圖獨立系數獲取電熔鎂爐特征類(lèi)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(wǎng)絡(luò )層級, 對不可信樣本進(jìn)行再認知以獲取最優(yōu)工況識別結果. 實(shí)驗結果表明, 所提方法較其他方法具有更優(yōu)的識別精度和可解釋性.
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關(guān)鍵詞:
- 電熔鎂爐 /
- 深層卷積隨機配置網(wǎng)絡(luò ) /
- 高斯差分卷積核 /
- 類(lèi)激活映射圖 /
- 可解釋性
Abstract: In order to solve the defects of generalization ability and weak interpretability of fused magnesium furnace working condition recognition model, an interpretable fused magnesium furnace abnormal working condition recognition method based on deep convolutional stochastic configuration networks (DCSCN) is proposed in this paper. Firstly, based on the supervised learning mechanism to generate Gaussian differential convolution kernel with physical meaning, an incremental method is used to construct a deep convolutional neural network (DCNN) to ensure that the recognition error converges step by step, and to avoid the process that back propagation algorithm iteratively finds the optimal convolutional kernel parameters. This paper defines channel feature map independent coefficients to obtain visualization results of fused magnesium furnace feature class activation mapping map, defines interpretable credibility measure to adaptively adjust deep convolutional stochastic configuration network layers, and recognizes untrustworthy samples to obtain optimal working condition recognition results. The experimental results show that the proposed method in this paper has better recognition accuracy and interpretability than other methods. -
圖 1 基于深層卷積隨機配置網(wǎng)絡(luò )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(wǎng)絡(luò )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(lèi)激活映射示意圖
Fig. 3 Schematic diagram of the class activation mapping based on feature map independence scores
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(guò)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(xiàn)
Fig. 8 Recognition accuracy curves under different convolutional kernel sizes
圖 9 強化學(xué)習訓練過(guò)程的平均獎勵曲線(xiàn)
Fig. 9 Average reward curves for training process of reinforcement learning methods
圖 10 不同卷積層類(lèi)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(xué)習的類(lèi)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(xué)習的可信識別樣本比例變化曲線(xiàn)
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(wǎng)絡(luò )模型的訓練樣本識別精度曲線(xiàn)
Fig. 13 Recognition accuracy curves of training samples for different network models
表 1 基于強化學(xué)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 ± 0.189 9.15 ± 0.331 83.24 ± 0.195 9.95 ± 0.216 10.30 ± 0.231 79.75 ± 0.108 強化學(xué)習 9.08 ± 0.082 10.14 ± 0.354 80.76 ± 0.228 10.51 ± 0.172 12.81 ± 0.390 76.68 ± 0.305 三層 本文方法 5.31 ± 0.239 1.96 ± 0.165 92.73 ± 0.166 5.24 ± 0.245 2.45 ± 0.203 92.31 ± 0.283 強化學(xué)習 7.36 ± 0.361 2.58 ± 0.313 90.06 ± 0.313 6.57 ± 0.361 3.61 ± 0.313 89.82 ± 0.329 
下載: 導出CSV
表 2 消融實(shí)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 ± 0.239 1.96 ± 0.165 92.73 ± 0.166 5.24 ± 0.245 2.45 ± 0.203 92.31 ± 0.283 未加入可解釋性模塊 5.57 ± 0.232 2.51 ± 0.223 91.92 ± 0.278 7.29 ± 0.173 1.59 ± 0.181 91.12 ± 0.347 未加入高斯卷積核 4.29 ± 0.274 4.51 ± 0.391 91.20 ± 0.264 3.45 ± 0.255 2.50 ± 0.329 90.54 ± 0.231 未加入可解釋性模塊以及高斯卷積核 6.02 ± 0.183 4.25 ± 0.231 89.73 ± 0.325 4.13 ± 0.242 6.73 ± 0.228 89.14 ± 0.179
下載: 導出CSV
表 3 不同高斯噪聲的實(shí)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 ± 0.239 1.96 ± 0.165 92.73 ± 0.166 5.24 ± 0.245 2.45 ± 0.203 92.31 ± 0.283 $\eta=0.6$模型 6.92 ± 0.232 2.21 ± 0.223 90.87 ± 0.206 7.19 ± 0.173 2.52 ± 0.181 90.29 ± 0.347 $\eta=0.9$模型 8.31 ± 0.423 2.29 ± 0.248 89.40 ± 0.297 7.45 ± 0.382 7.01 ± 0.274 85.54 ± 0.288
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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 SCN 14.21 ± 0.228 14.21 ± 0.228 76.14 ± 0.215 塊增量BSC 12.58 ± 0.285 10.57 ± 0.153 76.85 ± 0.233 2DSCN 6.49 ± 0.263 15.52 ± 0.303 77.99 ± 0.353 DeepSCN 9.04 ± 0.285 7.32 ± 0.075 83.64 ± 0.209 CNN 6.82 ± 0.376 5.46 ± 0.167 87.72 ± 0.231 貝葉斯網(wǎng)絡(luò )[6] 5.36 ± 0.268 4.72 ± 0.252 89.92 ± 0.256 CNN+LSTM[8] 6.91 ± 0.201 3.52 ± 0.184 89.57 ± 0.337 本文方法 5.24 ± 0.245 2.45 ± 0.203 92.31 ± 0.283
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表 5 不同識別模型的綜合性能對比
Table 5 Comprehensive performance comparison of different recognition models
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表 6 太陽(yáng)能電池板數據集實(shí)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$0.187 7.86$\pm$0.259 84.83$\pm$0.245 未加入可解釋性模塊 9.87$\pm$0.252 6.94$\pm$0.243 83.19$\pm$0.279 三層 本文方法 3.45$\pm$0.213 3.51$\pm$0.169 93.04$\pm$0.323 未加入可解釋性模塊 4.13$\pm$0.192 4.22$\pm$0.257 91.65$\pm$0.236
下載: 導出CSV
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