面向復雜工業(yè)過(guò)程的虛擬樣本生成綜述

湯健; 崔璨麟; 夏恒; 喬俊飛

doi:10.16383/j.aas.c221006

面向復雜工業(yè)過(guò)程的虛擬樣本生成綜述

doi: 10.16383/j.aas.c221006

湯健^{1, 2, 3,},
崔璨麟^{1, 2, 3,},
夏恒^{1, 2, 3,},
喬俊飛^{1, 2, 3,}

1.
北京工業(yè)大學(xué)信息學(xué)部北京 100124
2.
北京工業(yè)大學(xué)智慧環(huán)保北京實(shí)驗室北京 100124
3.
北京工業(yè)大學(xué)智能感知與自主控制教育部工程研究中心北京 100124

基金項目: 國家自然科學(xué)基金(62073006, 62173120), 北京市自然科學(xué)基金(4212032), 科技創(chuàng )新2030-“新一代人工智能”重大項目(2021ZD0112301, 2021ZD0112302)資助

詳細信息

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

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

夏恒：北京工業(yè)大學(xué)信息學(xué)部博士研究生. 主要研究方向為樹(shù)結構深/寬度學(xué)習結構設計與優(yōu)化, 城市固廢焚燒過(guò)程二噁英排放預測. E-mail: xiaheng@emails.bjut.edu.cn

喬俊飛：北京工業(yè)大學(xué)信息學(xué)部教授. 主要研究方向為污水處理過(guò)程智能控制, 神經(jīng)網(wǎng)絡(luò )結構設計與優(yōu)化. E-mail: junfeiq@bjut.edu.cn

計量
- 文章訪(fǎng)問(wèn)數: 1665
- HTML全文瀏覽量: 780
- PDF下載量: 405
- 被引次數: 0
出版歷程
- 收稿日期: 2022-12-30
- 錄用日期: 2023-05-18
- 網(wǎng)絡(luò )出版日期: 2023-08-14
- 刊出日期: 2024-04-26

A Survey of Virtual Sample Generation for Complex Industrial Processes

TANG Jian^{1, 2, 3
,},
CUI Can-Lin^{1, 2, 3
,},
XIA Heng^{1, 2, 3
,},
QIAO Jun-Fei^{1, 2, 3
,}

1.
Faculty of Information Technology, Beijing University of Technology, Beijing 100124
2.
Beijing Laboratory of Smart Environmental Protection, Beijing University of Technology, Beijing 100124
3.
Engineering Research Center of Intelligent Perception and Autonomous Control, Ministry of Education, Beijing University of Technology, Beijing 100124

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

摘要: 用于復雜工業(yè)過(guò)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(shù), 已成為解決上述問(wèn)題的主要手段之一, 但已有研究還存在缺乏理論支撐、分類(lèi)準則與應用邊界模糊等問(wèn)題. 本文在描述復雜工業(yè)過(guò)程難測運行指標和異常故障建模所存在問(wèn)題的基礎上, 梳理虛擬樣本定義及其內涵, 給出面向工業(yè)過(guò)程回歸與分類(lèi)問(wèn)題的VSG實(shí)現流程; 接著(zhù), 從樣本覆蓋區域、實(shí)現流程與推廣應用等方向進(jìn)行綜述; 然后, 分析討論VSG的下一步研究方向; 最后, 對全文進(jìn)行總結并給出未來(lái)挑戰.
- 復雜工業(yè)過(guò)程 /
- 虛擬樣本生成 /
- 數據驅動(dòng)建模 /
- 樣本覆蓋區域
Abstract: The modeling samples for difficulty to measure operation indexes and abnormal faults of complex industrial processes usually have the characteristics of sparse quantity, unbalanced distribution, and lack of connotation mechanism knowledge. Virtual sample generation (VSG) is a technology to expand the space and quantity of modeling samples and has become one of the main ways to solve the formerly mentioned difficulties. However, there are still some problems in the existing research results, such as the lack of theoretical support, the unclear of category criterion and the application boundary. First, the existing problems for difficulty to measure operational indexes and abnormal fault modeling of complex industrial processes are described. The definition of virtual samples and the connotation of virtual samples are combed, and the VSG implementation process for the regression and classification problems is provided. Second, the research status is summarized from the sample coverage area, implementation process, and application. Third, further research direction is analyzed and discussed. Finally, the summary and future challenges are given out.
- Complex industrial process /
- virtual sample generation (VSG) /
- data-driven modeling /
- sample coverage area

HTML全文

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

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圖 2 樣本輸入空間內虛擬與真實(shí)樣本間的關(guān)系

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

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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

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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

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圖 5 面向分類(lèi)問(wèn)題的虛擬與真實(shí)樣本間的關(guān)系

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

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圖 6 面向工業(yè)過(guò)程的VSG實(shí)現流程圖

Fig. 6 Flow chart of VSG for industrial process

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圖 7 VSG的研究現狀結構圖

Fig. 7 Structure diagram of VSG research status

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圖 8 GAN模型的結構

Fig. 8 Structure of GAN model

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圖 9 基于CGAN的VSG模型結構

Fig. 9 VSG model structure based on CGAN

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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

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圖 11 大趨勢擴散技術(shù)

Fig. 11 Mega-trend-diffusion technology

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圖 12 MD-MTD示意圖

Fig. 12 Schematic figure of multi-distribution MTD

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圖 13 面向回歸建模問(wèn)題的VSG應用統計結果

Fig. 13 VSG application statistical results for regression modeling problem

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

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

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表 1 面向分類(lèi)問(wèn)題的虛擬樣本評價(jià)指標

Table 1 Virtual sample evaluation index for classification problem

評價(jià)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(guān)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(guān)系數	[85]	2022
最大均值差異、KL散度、GAN測試值	[92]	2022

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表 2 面向回歸問(wèn)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]
	${x_2} \in \left[ {0,2\pi } \right]$	[59, 129]
$y = {{\rm{e}}^{(2x - 1)}}\sin \left[ {4\pi {{(x - 0.6)}^2}} \right] + \varepsilon ,{\rm{ }}\varepsilon \ \sim {\rm{N}}(0,0.002\;5)$	$x \in \left[ {0,1} \right]$	[20]
注: $\varepsilon $是為了更好地模擬實(shí)際工業(yè)過(guò)程的環(huán)境影響而添加的噪聲項.

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表 3 面向分類(lèi)問(wèn)題的VSG公開(kāi)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(xué)發(fā)布的位于軸承數據中心網(wǎng)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(wú)故障和滾動(dòng)體、內圈和外圈故障數據	[67, 69, 92, 132?133]
University of Connecticut	美國康涅狄格大學(xué)Jiong Tang團隊發(fā)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(tài)數據
Tennessee Eastman process	由美國伊士曼化學(xué)公司開(kāi)發(fā)的化學(xué)過(guò)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	[156?157]
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	[80]
西安交通大學(xué)Spectra Quest (SQ)	由西安交通大學(xué)SQ實(shí)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	[150]
數據集網(wǎng)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

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A1 VSG的研究成果統計與對比

A1 Statistics and comparison of VSG research results

分類(lèi)	子分類(lèi)	方法	年份	優(yōu)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(xué)習更加直觀(guān), 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(yè)過(guò)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(jià)值	[49]
		總線(xiàn)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(jīng)網(wǎng)絡(luò )	[59]
		AANN插值	2019	模型學(xué)習樣本的非線(xiàn)性分布關(guān)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(yōu)化選擇虛擬樣本	[61]
		多目標PSO	2022	函數模型, 多目標PSO優(yōu)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(guò)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(lèi)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	添加卷積層提取特征	[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(lèi)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(xiàn)性可分離問(wèn)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(shù)	2020	函數模型, 決策樹(shù)算法提取關(guān)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(wèn)題	[75]
		超球面空間 + 組發(fā)現技術(shù)	2007	函數模型, 由數據的結構生成虛擬樣本	[76]
		組發(fā)現技術(shù) + 純化過(guò)程	2020	函數模型, 純化過(guò)程剔除冗余樣本	[77]
		ACGAN	2020	對抗模型, 添加Dropout層防止過(guò)擬合, 添加卷積層提取更多特征	[79]
		ACWGAN-GP	2020	對抗模型, ACGAN的進(jìn)化版	[80]
		MAML + ACGAN	2021	對抗模型, MAML初始化和更新網(wǎng)絡(luò )使得生成過(guò)程更加穩定	[81]
		GAN + 多尺度CNN	2021	對抗模型, 生成模型需要改進(jìn)	[82]
		DCGAN + K-means	2021	對抗模型, K-means算法對模型改進(jìn)	[67]
		MoGAN	2021	對抗模型, 判別器既判斷樣本真假又充當分類(lèi)器和故障檢測器	[83]
		GAN + MSCNN	2021	對抗模型, 多GAN聯(lián)合生成	[82]
		貝葉斯優(yōu)化 + WGAN	2021	對抗模型, 貝葉斯優(yōu)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	對抗模型, AE結合GAN	[68]
		深度殘差網(wǎng)絡(luò ) + VAE + GAN	2021	對抗模型, 深度殘差網(wǎng)絡(luò )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(tài)隸屬度	1997	模糊集理論, 僅適用于擴展范圍對稱(chēng)情況	[94]
		DNN	2003	模糊集理論, 特征相關(guān)系數大于0.9才能計算擴展范圍	[95]
		MTD	2007	模糊集理論, 通過(guò)假設特征獨立不對稱(chēng)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(shù)算法結合	[98]
		神經(jīng)網(wǎng)絡(luò )MTD	2012	模糊集理論, 神經(jīng)網(wǎng)絡(luò )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(cháng)補短	[103]
		MTD + RWNN	2020	模糊集理論, 改進(jìn)分布 + 隱含層插值	[56]
		MTD + GA	2014	模糊集理論, 基于優(yōu)化算法搜尋虛擬樣本, 更合理	[46]
		TMIE + PSO	2016	模糊集理論, PSO優(yōu)化選擇虛擬樣本	[104]
		MTD + PSO	2021	模糊集理論, PSO優(yōu)化選擇虛擬樣本	[61]
	分布假設	IKDE	2006	高斯分布, 改進(jìn)KDE分布	[109]
		時(shí)序IKDE	2008	高斯分布, 用于時(shí)序數據	[110]
		SJDT	2016	高斯分布, SJDT將數據趨于正態(tài)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(lèi)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(yōu)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(lèi)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(tài)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(guò)程采用特定分布	[119]
	基于知識	FAGAN	2021	基于知識, 專(zhuān)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(shí)現流程之回歸問(wèn)題	過(guò)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(huì )減少樣本數量	[61]
		缺失值刪減和人工填充	2022		[15]
		缺失和異常值識別剔除	2020		[127]
		缺失和異常值識別剔除	2022		[64]
		LLE	2020	流形學(xué)習更加直觀(guān), 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]
		根據化工機理選擇特征	2018	機理知識獲取困難	[59]
		專(zhuān)家經(jīng)驗	2021	特定實(shí)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(guān)	[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(guān)系	[49]
		WGAN-GP	2022	引入GAN用于回歸	[64]
		CWGAN	2022	引入GAN用于回歸	[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(shí)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(zhì)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(yōu)化算法篩選	2021	引入優(yōu)化算法	[61]
		專(zhuān)家篩選	2021	具有主觀(guān)性	[49]
	虛擬樣本數量確定階段	信息熵	2019	引入信息熵	[131]
	虛擬樣本數量確定階段	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]
	特殊階段	三樣條插值 + ITNN	2021	先生成虛擬樣本輸出后匹配虛擬樣本輸入	[49]
面向VSG實(shí)現流程之分類(lèi)問(wèn)題	過(guò)程數據預處理階段	信號數據轉換為灰度圖	2022	借鑒圖像領(lǐng)域算法處理	[85]
	過(guò)程數據預處理階段	信號數據轉換為灰度圖	2022	借鑒圖像領(lǐng)域算法處理	[132]
	虛擬樣本輸入生成階段	重疊分割、旋轉和抖動(dòng)的數據增強	2021	緩解過(guò)擬合	[133]
		SMOTE + 粗糙集理論	2012	擴展范圍有限	[115]
		SMOTE + 決策樹(shù)	2020	決策樹(shù)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(guò)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(guò)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(wǎng)絡(luò ) + VAE + GAN	2021	深度殘差網(wǎng)絡(luò )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(lèi)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(lèi)別同時(shí)訓練, 復雜性高	[19]
		SMOTE + VAE	2018	基于樣本的遷移學(xué)習VSG	[134]
		自適應混合	2020		[135]
		遷移學(xué)習 + 插值	2022		[136]
		Fine-tuning + WGAN	2021	基于模型的遷移學(xué)習VSG	[137]
		遷移學(xué)習 + GAN	2022	基于模型的遷移學(xué)習VSG	[138]
	虛擬樣本質(zhì)量篩選階段	Wasserstein距離	2020	未給出評價(jià)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	2021		[66]
		Wasserstein距離, KL 散度, 歐氏距離,	2021		[67]
		皮爾遜相關(guān)系數	2021		[67]
		馬氏距離, 歐氏距離	2021	給出評價(jià)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	給出評價(jià)指標的具體限值篩選虛擬樣本	[69]
		皮爾遜相關(guān)系數	2022	未給出評價(jià)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	2022		[92]
	虛擬樣本數量確定階段	分類(lèi)復雜度確定虛擬樣本數量	1998	采用分類(lèi)復雜度確定虛擬樣本數量	[14]
面向VSG推廣應用的回歸問(wèn)題	石油化工	TMIE + PSO	2016	PSO優(yōu)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	提出隱含層插值生成虛擬樣本	[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(guò)回歸器匹配虛擬樣本輸出并同時(shí)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(yōu)化選擇虛擬樣本	[61]
		多目標PSO	2022	多目標PSO優(yōu)化選擇虛擬樣本和生成數量	[15]
	工業(yè)制造	GTD	2010	增量版的MTD	[97]
		TTD	2012	與樹(shù)算法結合	[98]
		MPV	2013	采用多分布	[112]
		模糊c均值聚類(lèi) + 箱線(xiàn)圖	2018	箱線(xiàn)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(yè)冶金	時(shí)頻變換 + FBP + 信息熵	2018	特定問(wèn)題采用特定方法	[6]
	礦業(yè)冶金	RWNN插值 + MD-MTD	2019	GA優(yōu)化選擇虛擬混合樣本	[145]
面向VSG推廣應用的分類(lèi)問(wèn)題	滾動(dòng)軸承故障診斷	遷移學(xué)習 + GAN	2020	遷移與GAN相結合	[146]
		PGDAE + DCN	2021	引入PGDAE	[147]
		元學(xué)習 + WAE	2021	元學(xué)習提高虛擬樣本質(zhì)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(shù)	2020	決策樹(shù)算法提取關(guān)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(wèn)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(yōu)化 + WGAN	2021	貝葉斯優(yōu)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(jìn)	[67]

下載: 導出CSV

A2 符號說(shuō)明

A2 Symbol description

縮寫(xiě)詞	英文全稱(chēng)	中文全稱(chēng)
VSG	Virtual sample generation	虛擬樣本生成
MSWI	Municipal solid waste incineration	城市固廢焚燒
DXN	Dioxin	二噁英
VAE	Variational autoencoder	變分自編碼器
GAN	Generative adversarial network	生成對抗網(wǎng)絡(luò )
FDD	Fault detection and diagnosis	故障檢測與診斷
IR	Imbalance ratio	不平衡比
SMOTE	Synthetic minority over-sampling technique	合成少數類(lèi)過(guò)采樣技術(shù)
MAPE	Mean absolute percentage error	平均絕對百分比誤差
LLE	Locally linear embedding	局部線(xiàn)性嵌入
BPNN	Back propagation neural network	反向傳播神經(jīng)網(wǎng)絡(luò )
Isomap	Isometric feature mapping	等距特征映射
t-SNE	t-distributed stochastic neighbor embedding	t分布隨機鄰域嵌入
RF	Random forest	隨機森林
RBF	Radial basis function	徑向基函數
CSI	Cubic spline interpolation	三樣條插值
ITNN	Input-training neural network	輸入訓練神經(jīng)網(wǎng)絡(luò )
RWNN	Random weight neural network	隨機權神經(jīng)網(wǎng)絡(luò )
AANN	Auto-associative neural network	自聯(lián)想神經(jīng)網(wǎng)絡(luò )
LOF	Local outlier factor	局部異常因子
CGAN	Conditional generative adversarial network	條件生成對抗網(wǎng)絡(luò )
CS-CGAN	Cycle structure conditional generative adversarial network	循環(huán)結構條件生成對抗網(wǎng)絡(luò )
FFT	Fast Fourier transform	快速傅里葉變換
SVM	Support vector machine	支持向量機
AC-GAN	Auxiliary classifier generative adversarial network	輔助分類(lèi)器生成對抗網(wǎng)絡(luò )
ACWGAN-GP	Auxiliary classier Wasserstein generative adversarial network with	具有梯度懲罰的輔助分類(lèi)Wasserstein生成對抗網(wǎng)絡(luò )
ACWGAN-GP	gradient penalty	具有梯度懲罰的輔助分類(lèi)Wasserstein生成對抗網(wǎng)絡(luò )
MAML	Model agnostic meta learning	模型無(wú)關(guān)元學(xué)習
CNN	Convolutional neural network	卷積神經(jīng)網(wǎng)絡(luò )
DCGAN	Deep convolutional generative adversarial network	深度卷積生成對抗網(wǎng)絡(luò )
MoGAN	Minority oversampling generative adversarial network	少數類(lèi)過(guò)采樣生成對抗網(wǎng)絡(luò )
AE	Autoencoder	自編碼器
CVAE-GAN	Conditional variational autoencoder generative adversarial network	條件變分自編碼器生成對抗網(wǎng)絡(luò )
LSGAN	Least squares generative adversarial network	最小二乘生成對抗網(wǎng)絡(luò )
DNN	Diffusion neural network	擴散神經(jīng)網(wǎng)絡(luò )
MTD	Mega-trend-diffusion	大趨勢擴散
GTD	Generalized-trend-diffusion	廣義趨勢擴散
TTD	Tree structure based trend diffusion	樹(shù)結構趨勢擴散
MD-MTD	Multi-distribution mega-trend-diffusion	多分布大趨勢擴散
KNN	K-nearest neighbor	K近鄰
AD-MTD	Advanced mega-trend-diffusion	改進(jìn)型大趨勢擴散
Hybrid-MTD	Hybrid mega-trend-diffusion	混合大趨勢擴散
GA	Genetic algorithm	遺傳算法
FBP	Feasibility-based programming	可行性的規劃
TMIE	Information-expanded based on triangular membership	基于三角隸屬度的信息擴散
PSO	Particle swarm optimization	粒子群優(yōu)化
IKDE	Improved kernel density estimation	改善核密度估計
SJDT	Small Johnson data transformation	小型約翰變換方法
MPV	Maximal p value	最大p值
AIC	Akaike information criterion	赤池信息準則
AICc	Corrected version of the akaike information criterion	修正版赤池信息準則
FID	Fuzzy-based information decomposition	基于模糊的信息分解
BIC	Bayesian information criterion	貝葉斯信息準則
FAGAN	Fault attributes generative adversarial network	故障屬性生成對抗網(wǎng)絡(luò )
SRWGAN	Semantic refinement Wasserstein generative adversarial network	語(yǔ)義細化Wasserstein生成對抗網(wǎng)絡(luò )
MOPSO	Multi-objective particle swarm optimization	多目標粒子群優(yōu)化
PGDAE	Predictive generative denoising autoencoder	預測生成去噪自編碼器
DCN	Deep coral network	深度珊瑚網(wǎng)絡(luò )
WAE	Wasserstein autoencoder	Wasserstein自編碼器
DSAN	Deep subdomain adaptation network	深度子域適應網(wǎng)絡(luò )
DFGN	Deep feature generating network	深度特征生成網(wǎng)絡(luò )
DACNN	Deep adversarial convolutional neural network	深度對抗卷積神經(jīng)網(wǎng)絡(luò )
BO	Bayesian optimization	貝葉斯優(yōu)化
DCGAN	Deep convolution generative adversarial network	深度卷積生成對抗網(wǎng)絡(luò )

下載: 導出CSV

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