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              面向復雜工業過程的虛擬樣本生成綜述

              湯健 崔璨麟 夏恒 喬俊飛

              湯健, 崔璨麟, 夏恒, 喬俊飛. 面向復雜工業過程的虛擬樣本生成綜述. 自動化學報, 2024, 50(4): 688?718 doi: 10.16383/j.aas.c221006
              引用本文: 湯健, 崔璨麟, 夏恒, 喬俊飛. 面向復雜工業過程的虛擬樣本生成綜述. 自動化學報, 2024, 50(4): 688?718 doi: 10.16383/j.aas.c221006
              Tang Jian, Cui Can-Lin, Xia Heng, Qiao Jun-Fei. A survey of virtual sample generation for complex industrial processes. Acta Automatica Sinica, 2024, 50(4): 688?718 doi: 10.16383/j.aas.c221006
              Citation: Tang Jian, Cui Can-Lin, Xia Heng, Qiao Jun-Fei. A survey of virtual sample generation for complex industrial processes. Acta Automatica Sinica, 2024, 50(4): 688?718 doi: 10.16383/j.aas.c221006

              面向復雜工業過程的虛擬樣本生成綜述

              doi: 10.16383/j.aas.c221006
              基金項目: 國家自然科學基金(62073006, 62173120), 北京市自然科學基金(4212032), 科技創新2030-“新一代人工智能”重大項目(2021ZD0112301, 2021ZD0112302)資助
              詳細信息
                作者簡介:

                湯?。罕本┕I大學信息學部教授. 主要研究方向為小樣本數據建模, 城市固廢處理過程智能控制. 本文通信作者. E-mail: freeflytang@bjut.edu.cn

                崔璨麟:北京工業大學信息學部碩士研究生. 主要研究方向為城市固廢焚燒過程風險預警, 虛擬樣本生成. E-mail: cuicanlin@emails.bjut.edu.cn

                夏恒:北京工業大學信息學部博士研究生. 主要研究方向為樹結構深/寬度學習結構設計與優化, 城市固廢焚燒過程二噁英排放預測. E-mail: xiaheng@emails.bjut.edu.cn

                喬俊飛:北京工業大學信息學部教授. 主要研究方向為污水處理過程智能控制, 神經網絡結構設計與優化. E-mail: junfeiq@bjut.edu.cn

              A Survey of Virtual Sample Generation for Complex Industrial Processes

              Funds: Supported by National Natural Science Foundation of China (62073006, 62173120), Beijing Natural Science Foundation (4212032), and National Key Research and Development Program of China (2021ZD0112301, 2021ZD0112302)
              More Information
                Author Bio:

                TANG Jian Professor at the Faculty of Information Technology, Beijing University of Technology. His research interest covers small sample data modeling and intelligent control of municipal solid waste treatment process. Corresponding author of this paper

                CUI Can-Lin Master student at the Faculty of Information Technology, Beijing University of Technology. His research interest covers risk warning of municipal solid waste incineration process and virtual sample generation

                XIA Heng Ph.D. candidate at the Faculty of Information Technology, Beijing University of Technology. His research interest covers structure design and optimization of tree-structured deep/broad learning and dioxin emission prediction of the municipal solid waste incineration process

                QIAO Jun-Fei Professor at the Faculty of Information Technology, Beijing University of Technology. His research interest covers intelligent control of wastewater treatment process, and structure design and optimization of neural networks

              • 摘要: 用于復雜工業過程難測運行指標和異常故障建模的樣本具有量少稀缺、分布不平衡以及內涵機理知識匱乏等特性. 虛擬樣本生成(Virtual sample generation, VSG)作為擴充建模樣本數量及其涵蓋空間的技術, 已成為解決上述問題的主要手段之一, 但已有研究還存在缺乏理論支撐、分類準則與應用邊界模糊等問題. 本文在描述復雜工業過程難測運行指標和異常故障建模所存在問題的基礎上, 梳理虛擬樣本定義及其內涵, 給出面向工業過程回歸與分類問題的VSG實現流程; 接著, 從樣本覆蓋區域、實現流程與推廣應用等方向進行綜述; 然后, 分析討論VSG的下一步研究方向; 最后, 對全文進行總結并給出未來挑戰.
              • 圖  1  Web of Science上的VSG論文數量與被引頻次

                Fig.  1  Number and citation frequency of articles on VSG in Web of Science

                圖  2  樣本輸入空間內虛擬與真實樣本間的關系

                Fig.  2  Relationship between virtual samples and real samples in sample input space

                圖  3  三維空間下的不同虛擬樣本輸入生成方法示意圖

                Fig.  3  Diagram of different virtual sample input generation methods in 3D space

                圖  4  映射模型生成虛擬樣本輸出流程圖

                Fig.  4  Flow chart of virtual sample output generation based on mapping model

                圖  5  面向分類問題的虛擬與真實樣本間的關系

                Fig.  5  Relationship between virtual samples and real samples for classification problem

                圖  6  面向工業過程的VSG實現流程圖

                Fig.  6  Flow chart of VSG for industrial process

                圖  7  VSG的研究現狀結構圖

                Fig.  7  Structure diagram of VSG research status

                圖  8  GAN模型的結構

                Fig.  8  Structure of GAN model

                圖  9  基于CGAN的VSG模型結構

                Fig.  9  VSG model structure based on CGAN

                圖  10  面向VSG的原始域、可擴展域和未知域的示意圖

                Fig.  10  Schematic diagram of original, extension, and unknown domain for VSG

                圖  11  大趨勢擴散技術

                Fig.  11  Mega-trend-diffusion technology

                圖  12  MD-MTD示意圖

                Fig.  12  Schematic figure of multi-distribution MTD

                圖  13  面向回歸建模問題的VSG應用統計結果

                Fig.  13  VSG application statistical results for regression modeling problem

                圖  14  2019 ~ 2022年面向故障診斷領域的VSG應用統計結果

                Fig.  14  VSG application statistical results for fault diagnosis on 2019 ~ 2022

                表  1  面向分類問題的虛擬樣本評價指標

                Table  1  Virtual sample evaluation index for classification problem

                評價指標文獻年份
                Wasserstein距離[139]2020
                KL散度、F-score、Kappa系數、GAN測試值[66]2021
                Wasserstein距離、KL散度、歐氏距離、皮爾遜相關系數[67]2021
                馬氏距離、歐氏距離[82]2021
                判別概率、最大均值差異、KL散度[69]2022
                皮爾遜相關系數[85]2022
                最大均值差異、KL散度、GAN測試值[92]2022
                下載: 導出CSV

                表  2  面向回歸問題VSG的合成數據集

                Table  2  Synthetic datasets of VSG for regression problem

                基準函數取值空間文獻
                $y = \left\{ {\begin{aligned} &{\sin x/x,{\rm{ if }}x \ne 0}\\ &{1,{\rm{ if }}x = 0} \end{aligned}} \right.$$x \in \left[ { - 2\pi ,2\pi } \right]$[49]
                $\begin{aligned} y = \;&2.077\;5 + 9.045\;46 \times \left( {{{10}^{ - 1}}} \right){x_1} + x_2^2 + \cos \left( {{x_3}} \right) + 1.355\;6 \times \left( {1.5 \times \left( {1 - {x_4}} \right)} \right){\rm{ }} +\\ &x_5^3 + {x_6} - 2.571\;51{x_7} - 5.097\;36 \times \left( {{{10}^{ - 1}}} \right) \times \left( {x_8^2} \right)\end{aligned}$$x \in \left[ {0,1} \right]$[53]
                $\begin{aligned}y = \;&0.415\sin {x_1} - 0.312x_2^2 + 1/\left( {1 + {{\rm{e}}^{ - {x_3}}}} \right) + \cos x_4^3 + 0.66{{\rm{e}}^{1 - x_5^{0.5}}}\sin {x_5}{\rm{ }} \;-\\ &\cos {x_6}\ln \left( {1/\cos {x_6}} \right) + 0.38\tanh {x_7} + \left( {1 - x_8^3} \right)\cos x_8^3\end{aligned}$${\rm{ }}x \in \left[ {0,1} \right]$[54]
                $\begin{aligned} &y = x + \varepsilon ,{\rm{ } }\varepsilon \ \sim {\rm{N} }\left( {0,{ {0.05}^2} } \right)\\ &y = x + \varepsilon ,{\rm{ } }\varepsilon \ \sim {\rm{N} }\left( {0,0.01{x^2} } \right)\\ &y = \;x + 0.2\sin \left( {20x} \right) + \varepsilon ,{\rm{ } }\varepsilon \ \sim {\rm{N} }\left( {0,{ {0.05}^2} } \right) \end{aligned}$$x \in \left[ {0,1} \right]$[55]
                $\begin{aligned} y =\;& 1.335\;6 \times \left( {1.5\left( {1 - {x_1} } \right)} \right) + { {\rm{e} }^{2{x_1} - 1} }\sin \left( {3\pi { {\left( { {x_1} - 0.6} \right)}^2} } \right){\rm{ } } +\\ &{ {\rm{e} }^{3\left( { {x_2} - 0.5} \right)} }\sin \left( {4\pi { {\left( { {x_2} - 0.9} \right)}^2} } \right)\end{aligned}$$x \in \left[ {0,1} \right]$[57, 64, 127]
                $y = \sin \left( {{x_1}} \right) + \cos \left( {{x_2}} \right) + \sin \left( {{x_1}} \right) \times \cos \left( {{x_2}} \right)$${x_1} \in \left[ { - \pi ,\pi } \right]$[59, 129]
                ${x_2} \in \left[ {0,2\pi } \right]$
                $y = {{\rm{e}}^{(2x - 1)}}\sin \left[ {4\pi {{(x - 0.6)}^2}} \right] + \varepsilon ,{\rm{ }}\varepsilon \ \sim {\rm{N}}(0,0.002\;5)$$x \in \left[ {0,1} \right]$[20]
                注: $\varepsilon $是為了更好地模擬實際工業過程的環境影響而添加的噪聲項.
                下載: 導出CSV

                表  3  面向分類問題的VSG公開數據集

                Table  3  Public datasets of VSG for classification problem

                數據集數據集信息文獻
                Case Western Reserve University由美國凱斯西儲大學發布的位于軸承數據中心網站的軸承故障[67, 69, 92, 132?133]
                (CWRU)軸承故障數據集1數據集, 包含無故障和滾動體、內圈和外圈故障數據
                University of Connecticut美國康涅狄格大學Jiong Tang團隊發布的齒輪箱故障數據集, [85]
                (UoC)齒輪箱故障包括健康工況、缺齒、齒根裂紋、齒面剝落以及
                數據集2不同程度齒尖破損狀態數據
                Tennessee Eastman process由美國伊士曼化學公司開發的化學過程模擬平臺生成的[156?157]
                (TEP)數據集3數據集, 包括正常工況和21種異常工況數據
                IEEE PHM 2009齒輪箱故障由2009年的IEEE PHM挑戰賽提供的齒輪箱故障[80]
                數據集4數據集, 包含健康、缺齒、齒裂等8種工況
                西安交通大學Spectra Quest (SQ)由西安交通大學SQ實驗平臺得到的電機軸承外圈[150]
                軸承故障數據集5和內圈故障數據集
                數據集網址:
                1 https://engineering.case.edu/bearingdatacenter/download-data-file
                2 https://figshare.com/articles/dataset/Gear_Fault_Data/6127874/1
                3 http://depts.washington.edu/control/LARRY/TE/download.html
                4 http://www.phmsociety.org/references/datasets
                5 https://github.com/sliu7102/SQ-dataset-with-variable-speed-for-fault-diagnosis
                下載: 導出CSV

                A1  VSG的研究成果統計與對比

                A1  Statistics and comparison of VSG research results

                分類 子分類 方法 年份 優劣 文獻
                面向樣本覆蓋區域之原始域樣本空間
                回歸VSG
                特征工程 LLE + BPNN 2020 特征變換, 流形學習更加直觀, 但特征失去物理含義 [52]
                Isomap + 插值法 2020 [53]
                t-SNE + RF 2021 [54]
                機理 2021 特征選擇, 工業過程知識獲取困難 [55]
                兩者結合 2020 綜合特征變換與選擇, 具有較強的定制化特性 [56]
                樣本工程 空間投影 + RBF 2021 函數模型, 空間投影具有新穎性 [57]
                數據趨勢 2021 函數模型, 提出的稀疏假設和集中假設具有參考價值 [49]
                總線拓撲結構插值 2023 函數模型, 有效控制插值位置 [58]
                RWNN插值法 2018 函數模型, 基于神經網絡 [59]
                AANN插值 2019 模型學習樣本的非線性分布關系 [60]
                RWNN + 等間隔插值法 2020 對小樣本難以有效 [56]
                MTD + PSO 2021 函數模型, PSO優化選擇虛擬樣本 [61]
                多目標PSO 2022 函數模型, 多目標PSO優化選擇虛擬樣本和生成數量 [15]
                LOF + K-means + GAN 2021 對抗模型, 插值生成輸出, CGAN生成輸入 [20]
                雙GAN 2022 對抗模型, 兩種GAN分別負責輸入和
                輸出的生成, 復雜性高
                [64]
                回歸器 + CWGAN 2022 對抗模型, 通過回歸器匹配虛擬樣本輸出并共同訓練 [65]
                面向樣本覆蓋區域之原始域樣本空間的分類VSG 特征工程 添加編碼器 2020 采用編碼器提取特征 [68]
                添加卷積層 2021 添加卷積層提取特征 [66]
                添加卷積層 2021 [67]
                添加自注意 2022 添加自注意力模型增強特征 [69]
                面向樣本覆蓋區域之原始域樣本空間的分類VSG 樣本工程 基于加權核的SMOTE 2018 函數模型, 解決SMOTE算法在高IR下
                的非線性可分離問題
                [71]
                Minkowski距離替換歐氏距離 2019 函數模型, 有效生成高維虛擬樣本 [72]
                SMOTE + 決策樹 2020 函數模型, 決策樹算法提取關鍵規則 [73]
                SMOTE + SVM 2020 函數模型, 支持向量邊界生成虛擬樣本 [74]
                范圍控制SMOTE 2021 函數模型, 有效地緩解范圍偏移和邊界樣本重疊等問題 [75]
                超球面空間 + 組發現技術 2007 函數模型, 由數據的結構生成虛擬樣本 [76]
                組發現技術 + 純化過程 2020 函數模型, 純化過程剔除冗余樣本 [77]
                ACGAN 2020 對抗模型, 添加Dropout層防止過擬合, 添加卷積層
                提取更多特征
                [79]
                ACWGAN-GP 2020 對抗模型, ACGAN的進化版 [80]
                MAML + ACGAN 2021 對抗模型, MAML初始化和更新網絡使得生成過程
                更加穩定
                [81]
                GAN + 多尺度CNN 2021 對抗模型, 生成模型需要改進 [82]
                DCGAN + K-means 2021 對抗模型, K-means算法對模型改進 [67]
                MoGAN 2021 對抗模型, 判別器既判斷樣本真假又
                充當分類器和故障檢測器
                [83]
                GAN + MSCNN 2021 對抗模型, 多GAN聯合生成 [82]
                貝葉斯優化 + WGAN 2021 對抗模型, 貝葉斯優化策略自適應調節判別器參數 [86]
                WGAN + LSTM-FCN 2022 對抗模型, 結合LSTM [84]
                AE + GAN 2019 對抗模型, AE結合GAN [89]
                VAE + GAN 2020 [68]
                深度殘差網絡 + VAE + GAN 2021 對抗模型, 深度殘差網絡提高模型性能 [90]
                AE + LSGAN 2022 對抗模型, 暹羅編碼器計算特征殘差 [91]
                CVAEGAN-SM 2022 對抗模型, 生成器中加入自調制機制 [92]
                堆疊AE + WGAN 2023 對抗模型, 提升了模型的生成能力 [93]
                面向樣本覆蓋區域之擴展域樣本空間的
                回歸VSG
                集合理論 正態隸屬度 1997 模糊集理論, 僅適用于擴展范圍對稱情況 [94]
                DNN 2003 模糊集理論, 特征相關系數大于0.9才能計算擴展范圍 [95]
                MTD 2007 模糊集理論, 通過假設特征獨立不對稱地擴散特征范圍 [96]
                GTD 2010 模糊集理論, 增量版的MTD [97]
                TTD 2012 模糊集理論, 與樹算法結合 [98]
                神經網絡MTD 2012 模糊集理論, 神經網絡與MTD結合 [99]
                MD-MTD 2016 模糊集理論, 三角和均勻分布組合的多分布 [100]
                KNN + MTD 2022 模糊集理論, KNN確保合理的擴展范圍 [101]
                K-means + MTD 2022 模糊集理論, K-means解決屬性冗余 [102]
                AD-MTD + MD-MTD 2019 模糊集理論, 多種算法結合取長補短 [103]
                MTD + RWNN 2020 模糊集理論, 改進分布 + 隱含層插值 [56]
                MTD + GA 2014 模糊集理論, 基于優化算法搜尋虛擬樣本, 更合理 [46]
                TMIE + PSO 2016 模糊集理論, PSO優化選擇虛擬樣本 [104]
                MTD + PSO 2021 [61]
                分布假設 IKDE 2006 高斯分布, 改進KDE分布 [109]
                時序IKDE 2008 高斯分布, 用于時序數據 [110]
                SJDT 2016 高斯分布, SJDT將數據趨于正態分布 [111]
                MPV 2013 非高斯分布, 多樣本分布 [112]
                假設檢驗 2019 非高斯分布, 先聚類再估計 [113]
                基于知識 多目標PSO 2022 基于知識確定輸出擴展域下限, 多目標PSO優化選擇虛擬樣本和生成數量 [15]
                面向樣本覆蓋區域之擴展域樣本空間的
                分類VSG
                集合理論 FID 2017 模糊集理論, 既生成虛擬樣本又填充缺失 [114]
                SMOTE + 粗糙集理論 2012 粗糙集理論, 擴展范圍有限 [115]
                三支決策 2018 粗糙集理論, 未精準計算擴展范圍 [116]
                分布假設 假設分布 2010 高斯分布, 計算數據的均值和方差確定高斯分布 [43]
                假設分布 2022 高斯分布, AIC和BIC自適應確定高斯分布參數 [117]
                SVM 2013 非高斯分布, 狀態函數采樣生成虛擬樣本 [118]
                K-means + Weibull分布 2014 非高斯分布, 特定過程采用特定分布 [119]
                基于知識 FAGAN 2021 基于知識, 專家知識定義的故障屬性作為輔助信息以
                使得生成樣本
                [123]
                面向VSG實現流程之回歸問題 過程數據預處理階段 缺失值刪減和人工填充 2021 有效減少缺失和異常值對數據的影響但會減少樣本數量 [61]
                2022 [15]
                缺失和異常值識別剔除 2020 [127]
                2022 [64]
                LLE 2020 流形學習更加直觀, 特征失去物理含義 [52]
                Isomap 2020 [53]
                t-SNE 2021 [54]
                根據化工機理選擇特征 2017 機理知識獲取困難 [128]
                2018 [59]
                專家經驗 2021 特定實驗知識 [61]
                虛擬樣本輸入生成階段 歐氏距離識別稀疏區域 2020 引入歐氏距離 [127]
                投影最大間距識別稀疏 2021 引入投影最大間距 [57]
                可視化樣本分布識別稀疏區域 2020 可視化, 直觀 [52]
                2020 [53]
                2021 [54]
                稀疏性和集中性假設 2021 確定稀疏和密集區域關系 [49]
                WGAN-GP 2022 引入GAN用于回歸 [64]
                CWGAN 2022 [65]
                MTD 2007 確定虛擬樣本輸入的擴展域范圍后插值 [96]
                TMIE + PSO 2016 [104]
                流形子空間 + MTD 2021 [129]
                虛擬樣本輸出生成階段 RWNN映射模型 2018 映射模型的性能受限于小樣本 [59]
                2016 [104]
                2021 [129]
                BPNN映射模型 2020 [52]
                RF映射模型 2021 [54]
                RBF映射模型 2021 [57]
                CS-CGAN匹配輸出 2022 匹配模型與虛擬樣本輸入同時訓練 [64]
                回歸器匹配輸出 2022 [65]
                分位數回歸器匹配輸出 2021 [55]
                虛擬樣本質量篩選階段 模型誤差小于10%篩選 2014 受限于小樣本建模性能 [46]
                隸屬度的似然估計篩選 2018 引入似然估計 [130]
                PSO優化算法篩選 2021 引入優化算法 [61]
                專家篩選 2021 具有主觀性 [49]
                虛擬樣本數量確定階段 信息熵 2019 引入信息熵 [131]
                稀疏和集中假設 2021 引入各種假設 [49]
                特殊階段 LOF + CGAN 2021 先生成虛擬樣本輸出后匹配虛擬樣本輸入 [20]
                三樣條插值 + ITNN 2021 [49]
                面向VSG實現流程之分類問題 過程數據預處理階段 信號數據轉換為灰度圖 2022 借鑒圖像領域算法處理 [85]
                2022 [132]
                虛擬樣本輸入生成階段 重疊分割、旋轉和抖動的
                數據增強
                2021 緩解過擬合 [133]
                SMOTE + 粗糙集理論 2012 擴展范圍有限 [115]
                SMOTE + 決策樹 2020 決策樹算法提取運行規則 [73]
                SMOTE + SVM 2020 引入支持向量機邊界 [74]
                Minkowski距離替換歐氏距離 2019 可以有效地生成高維虛擬樣本 [72]
                范圍控制SMOTE 2021 通過控制生成范圍減少邊界重疊樣本 [75]
                WGAN + LSTM-FCN 2022 引入LSTM [84]
                CWGAN-GP + FDGRU 2022 增加梯度懲罰項和條件信息 [85]
                Pull-away損失函數GAN 2022 添加自注意力模型增強特征 [69]
                ACGAN 2020 添加Dropout層防止過擬合, 添加卷積層提取更多特征 [79]
                ACGAN + CVAE 2020 引入CVAE [68]
                深度殘差網絡 + VAE + GAN 2021 深度殘差網絡提高模型性能 [90]
                MoGAN 2021 判別器包含真假判斷、故障診斷和故障分類三種功能 [83]
                CVAEGAN-SM 2022 生成器加入自調制機制 [92]
                并行GAN 2020 對應多類別同時訓練, 復雜性高 [19]
                SMOTE + VAE 2018 基于樣本的遷移學習VSG [134]
                自適應混合 2020 [135]
                遷移學習 + 插值 2022 [136]
                Fine-tuning + WGAN 2021 基于模型的遷移學習VSG [137]
                遷移學習 + GAN 2022 [138]
                虛擬樣本質量篩選階段 Wasserstein距離 2020 未給出評價指標的具體限值篩選虛擬樣本 [139]
                KL散度, F-score, 2021 [66]
                Kappa系數, GAN測試值
                Wasserstein距離, KL
                散度, 歐氏距離,
                2021 [67]
                皮爾遜相關系數
                馬氏距離, 歐氏距離 2021 給出評價指標的具體限值篩選虛擬樣本 [82]
                判別概率, 最大均值差異, KL散度 2022 [69]
                皮爾遜相關系數 2022 未給出評價指標的具體限值篩選虛擬樣本 [85]
                最大均值差異, KL散度, 2022 [92]
                GAN測試值
                虛擬樣本數量確定階段 分類復雜度確定虛擬樣本數量 1998 采用分類復雜度確定虛擬樣本數量 [14]
                面向VSG推廣應用的回歸問題 石油化工 TMIE + PSO 2016 PSO優化選擇 [104]
                RWNN插值法 2018 提出隱含層插值生成虛擬樣本 [59]
                Isomap + 插值法 2020 [53]
                分位數回歸器匹配輸出 2021 提出分位數回歸匹配輸出 [55]
                回歸器 + CWGAN 2022 通過回歸器匹配虛擬樣本輸出并同時訓練 [65]
                固廢焚燒 兩者結合 2020 具有較強的定制化特性 [56]
                MTD + PSO 2021 PSO優化選擇虛擬樣本 [61]
                多目標PSO 2022 多目標PSO優化選擇虛擬樣本和生成數量 [15]
                工業制造 GTD 2010 增量版的MTD [97]
                TTD 2012 與樹算法結合 [98]
                MPV 2013 采用多分布 [112]
                模糊c均值聚類 + 箱線圖 2018 箱線圖確定擴展范圍 [141]
                假設分布 2022 AIC和BIC自適應確定高斯分布參數 [117]
                礦業冶金 時頻變換 + FBP +
                信息熵
                2018 特定問題采用特定方法 [6]
                RWNN插值 + MD-MTD 2019 GA優化選擇虛擬混合樣本 [145]
                面向VSG推廣應用的分類問題 滾動軸承
                故障診斷
                遷移學習 + GAN 2020 遷移與GAN相結合 [146]
                PGDAE + DCN 2021 引入PGDAE [147]
                元學習 + WAE 2021 元學習提高虛擬樣本質量 [66]
                CVAEGAN-SM 2022 生成器加入自調制機制 [92]
                DSAN 2022 自注意模塊增強深度特征 [132]
                GAN 2022 常數Q轉換將信號轉換為頻譜圖, 均方差替換交叉熵 [148]
                ACGAN 2022 引入ACGAN [149]
                特征增強GAN 2022 自注意模塊增強深度特征 [69]
                DFGN 2021 可用于零樣本故障診斷 [150]
                變壓器
                故障診斷
                SMOTE + 決策樹 2020 決策樹算法提取關鍵規則 [73]
                SMOTE + SVM 2020 提出支持向量邊界生成樣本 [74]
                CWGAN-GP 2020 引入梯度懲罰 [151]
                AE + LSGAN 2022 暹羅編碼器計算特征殘差 [91]
                渦輪機
                故障診斷
                GAN 2019 結合GAN與具體問題 [152]
                DACNN 2019 [153]
                VAE + GAN 2019 [89]
                1D-CNN GAN 2019 虛擬樣本輸出和故障診斷組合模型 [154]
                齒輪箱
                故障診斷
                ACGAN + CVAE 2020 引入CVAE [68]
                貝葉斯優化 + WGAN 2021 貝葉斯優化策略自適應調節判別器參數 [86]
                DCGAN + K-means 2021 K-means算法對模型改進 [67]
                下載: 導出CSV

                A2  符號說明

                A2  Symbol description

                縮寫詞英文全稱中文全稱
                VSGVirtual sample generation虛擬樣本生成
                MSWIMunicipal solid waste incineration城市固廢焚燒
                DXNDioxin二噁英
                VAEVariational autoencoder變分自編碼器
                GANGenerative adversarial network生成對抗網絡
                FDDFault detection and diagnosis故障檢測與診斷
                IRImbalance ratio不平衡比
                SMOTESynthetic minority over-sampling technique合成少數類過采樣技術
                MAPEMean absolute percentage error平均絕對百分比誤差
                LLELocally linear embedding局部線性嵌入
                BPNNBack propagation neural network反向傳播神經網絡
                IsomapIsometric feature mapping等距特征映射
                t-SNEt-distributed stochastic neighbor embeddingt分布隨機鄰域嵌入
                RFRandom forest隨機森林
                RBFRadial basis function徑向基函數
                CSICubic spline interpolation三樣條插值
                ITNNInput-training neural network輸入訓練神經網絡
                RWNNRandom weight neural network隨機權神經網絡
                AANNAuto-associative neural network自聯想神經網絡
                LOFLocal outlier factor局部異常因子
                CGANConditional generative adversarial network條件生成對抗網絡
                CS-CGANCycle structure conditional generative adversarial network循環結構條件生成對抗網絡
                FFTFast Fourier transform快速傅里葉變換
                SVMSupport vector machine支持向量機
                AC-GANAuxiliary classifier generative adversarial network輔助分類器生成對抗網絡
                ACWGAN-GPAuxiliary classier Wasserstein generative adversarial network with具有梯度懲罰的輔助分類Wasserstein生成對抗網絡
                gradient penalty
                MAMLModel agnostic meta learning模型無關元學習
                CNNConvolutional neural network卷積神經網絡
                DCGANDeep convolutional generative adversarial network深度卷積生成對抗網絡
                MoGANMinority oversampling generative adversarial network少數類過采樣生成對抗網絡
                AEAutoencoder自編碼器
                CVAE-GANConditional variational autoencoder generative adversarial network條件變分自編碼器生成對抗網絡
                LSGANLeast squares generative adversarial network最小二乘生成對抗網絡
                DNNDiffusion neural network擴散神經網絡
                MTDMega-trend-diffusion大趨勢擴散
                GTDGeneralized-trend-diffusion廣義趨勢擴散
                TTDTree structure based trend diffusion樹結構趨勢擴散
                MD-MTDMulti-distribution mega-trend-diffusion多分布大趨勢擴散
                KNNK-nearest neighborK近鄰
                AD-MTDAdvanced mega-trend-diffusion改進型大趨勢擴散
                Hybrid-MTDHybrid mega-trend-diffusion混合大趨勢擴散
                GAGenetic algorithm遺傳算法
                FBPFeasibility-based programming可行性的規劃
                TMIEInformation-expanded based on triangular membership基于三角隸屬度的信息擴散
                PSOParticle swarm optimization粒子群優化
                IKDEImproved kernel density estimation改善核密度估計
                SJDTSmall Johnson data transformation小型約翰變換方法
                MPVMaximal p value最大p
                AICAkaike information criterion赤池信息準則
                AICcCorrected version of the akaike information criterion修正版赤池信息準則
                FIDFuzzy-based information decomposition基于模糊的信息分解
                BICBayesian information criterion貝葉斯信息準則
                FAGANFault attributes generative adversarial network故障屬性生成對抗網絡
                SRWGANSemantic refinement Wasserstein generative adversarial network 語義細化Wasserstein生成對抗網絡
                MOPSOMulti-objective particle swarm optimization多目標粒子群優化
                PGDAEPredictive generative denoising autoencoder預測生成去噪自編碼器
                DCNDeep coral network深度珊瑚網絡
                WAEWasserstein autoencoderWasserstein自編碼器
                DSANDeep subdomain adaptation network深度子域適應網絡
                DFGNDeep feature generating network深度特征生成網絡
                DACNNDeep adversarial convolutional neural network深度對抗卷積神經網絡
                BOBayesian optimization貝葉斯優化
                DCGANDeep convolution generative adversarial network深度卷積生成對抗網絡
                下載: 導出CSV
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                        出版歷程
                        • 收稿日期:  2022-12-30
                        • 錄用日期:  2023-05-18
                        • 網絡出版日期:  2023-08-14
                        • 刊出日期:  2024-04-26

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