钢铁生产过程能耗预测与调度优化研究
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摘要
钢铁生产流程是一个典型的高温、离散和连续混合的物理化学变化过程,具有多因素、多工序、多工位、强耦合、非线性等特点。高能耗是钢铁冶炼生产的主要问题,开展节能降耗不仅有利于节约生产成本,也有助于实现低碳加工。钢铁生产物流能耗预测,可以为钢铁企业制定能源总体规划方案提供支撑作用；在钢铁生产过程中,合理高效的生产调度对降低能耗和物耗,提高产品的品质,加快循环生产,降低生产成本有着极其重要的意义；此外,对钢铁生产过程中进行在线检测,及时发现故障,为避免浪费,节约能耗和物耗成本有着重要作用。本文将围绕钢铁生产过程的物流能耗预测,生产过程调度优化和生产过程故障预测等关键问题开展研究。主要完成了以下研究工作：
(1)针对钢铁生产流程中能耗预测模型建立困难、预测精度较低等问题,提出了一种基于蚁群优化的小波神经网络的钢铁生产物流能耗预测模型,首先对钢铁生产过程以及影响生产能耗的因素进行分析,确定输入参数构成特征空间,然后利用小波变换重构特征空间,接着利用神经网络模型建立能耗预测模型,最后采用蚁群算法对求解过程进行优化。在炼铁、炼钢以及轧钢工序的能耗预测实验表明,提出的方法具有较好的普适性的同时,提高了预测精度,为钢铁企业提前了解能耗需求提供了指导。
(2)针对钢铁生产过程中,炼钢-连铸关键工序存在多目标、多约束和不确定性等调度难题,提出一种基于遗传粒子群算法的炼钢-连铸过程的调度优化算法,首先炼钢-连铸生产工艺的约束条件下,建立以节能降耗、质量可靠、生产有序的条件下的最小化加工时间为优化目标的调度优化模型,利用粒子群算法收敛速度快与遗传算法全局搜索能力强的特点进行优化设计和参数求解,建立优化调度模型。实验结果表明,该算法是一种有效的炼钢-连铸生产调度优化方法,能够编制出实现连浇的可执行的炼钢作业计划,并且可以降低炉次在各工序间等待造成的损失,减小加工流程时间,从而达到降低能耗成本目的。
(3)为了解决在轧钢过程中进行故障检测或诊断存在困难的问题,提出了一种基于多核学习理论的钢铁生产轧钢过程在线故障检测模型。首先针对学习样本建立核主元分析与支持向量数据域描述模型,然后基于T2、Q统计量,以及数据域描述包络情况对轧钢过程进行初步识别,最后采用基于多分类多核最小二次支持向量预测模型对初识结果进行细分类,识别故障级别。本文利用上述模型对轧钢加热炉故障和机组故障进行了试验,结果表明,该方法能有效检测钢铁生产轧钢过程的故障。
Steel production process is a typical high temperature, discrete and continuous mixing process of physical and chemical changes, with multi-factor, multi-process, multi-position, strong coupling and nonlinear characteristics. High energy consumption is the main problem in the process of steel smelting production. Energy conservation and consumption is conducive not only to reduce production costs, but also to achieve a low-carbon processing purpose. Prediction of energy consumption in iron and steel production logistics can provide support for the iron and steel enterprises to formulate an energy overall plan; In the process of steel production, rational and efficient production scheduling has an extremely important significance for reducing energy and material consumption, improving product quality, accelerating the production cycle and reducing production costs; In addition, it plays an important role to online testing, failures detecting, avoid wasting and save energy and material consumption cost in the process of steel production. This dissertation covers research on prediction of logistics energy consumption in the process of steel production, scheduling optimization in production process, failures prediction in production process and so on. Mainly including the following three aspects:
(1) For the issues, such as it is difficult to establish energy forecasting model and prediction accuracy is low, this dissertation proposes a model of steel production logistics and energy prediction based on ant colony optimization wavelet neural network. At first, we analyze steel production process and the factors that impact production energy, determine the input parameters constitute feature space. Then reconstruct feature space using wavelet transform, model energy consumption prediction with neural network model. Finally optimize the solution process using ant colony algorithm. The energy consumption prediction experiments in iron-making, steel-making and rolling process show that the proposed method has better universality, at the same time it improves the prediction accuracy and provide guidance for the steel enterprises understanding energy needs in advance.
(2) For the problems that in the process of steel production, there exists multi-objective, multi-constrained and uncertainty in key process of steelmaking continuous casting, we propose a scheduling optimization algorithm based on genetic particle swarm optimization in steel-making and continuous casting process. Firstly, under the constraint condition of steel-making and continuous casting production process, establish scheduling optimization model to minimize the processing time of the optimization target with this understanding of energy saving, reliable quality and production ordered. Then, utilize the characteristics of fast convergence of particle swarm and global search ability of genetic algorithm to optimize the design and solving parameters. And finally, establish optimal scheduling model. Experimental results indicate that the proposed algorithm is an efficient scheduling optimization method in steel-making and continuous casting production, which is capable of compiling the executable steel-making operation plan to achieve continuous casting. And it can also reduce the loss which was made by the oven waiting time between the various processes; reduce the processing flow time, to reduce energy costs.
In order to solve the difficult problems of failures detection or diagnosis in rolling process, we propose a online failure detection model in rolling steel production process based on multi-core learning theory. First, for learning samples, build a nuclear principal component analysis and support vector data description model. And then, preliminary identify the rolling process based on T2, Q statistic and data domain description envelope. Finally, make fine classification for the first results based on multi-core minimum secondary support vector prediction model, and identify the failure categories. We utilize the above model to do experiments for rolling mill furnace failures and testing unit failures, and the results show that the method can effectively detect failures in the process of rolling steel production.
(1)针对钢铁生产流程中能耗预测模型建立困难、预测精度较低等问题,提出了一种基于蚁群优化的小波神经网络的钢铁生产物流能耗预测模型,首先对钢铁生产过程以及影响生产能耗的因素进行分析,确定输入参数构成特征空间,然后利用小波变换重构特征空间,接着利用神经网络模型建立能耗预测模型,最后采用蚁群算法对求解过程进行优化。在炼铁、炼钢以及轧钢工序的能耗预测实验表明,提出的方法具有较好的普适性的同时,提高了预测精度,为钢铁企业提前了解能耗需求提供了指导。
(2)针对钢铁生产过程中,炼钢-连铸关键工序存在多目标、多约束和不确定性等调度难题,提出一种基于遗传粒子群算法的炼钢-连铸过程的调度优化算法,首先炼钢-连铸生产工艺的约束条件下,建立以节能降耗、质量可靠、生产有序的条件下的最小化加工时间为优化目标的调度优化模型,利用粒子群算法收敛速度快与遗传算法全局搜索能力强的特点进行优化设计和参数求解,建立优化调度模型。实验结果表明,该算法是一种有效的炼钢-连铸生产调度优化方法,能够编制出实现连浇的可执行的炼钢作业计划,并且可以降低炉次在各工序间等待造成的损失,减小加工流程时间,从而达到降低能耗成本目的。
(3)为了解决在轧钢过程中进行故障检测或诊断存在困难的问题,提出了一种基于多核学习理论的钢铁生产轧钢过程在线故障检测模型。首先针对学习样本建立核主元分析与支持向量数据域描述模型,然后基于T2、Q统计量,以及数据域描述包络情况对轧钢过程进行初步识别,最后采用基于多分类多核最小二次支持向量预测模型对初识结果进行细分类,识别故障级别。本文利用上述模型对轧钢加热炉故障和机组故障进行了试验,结果表明,该方法能有效检测钢铁生产轧钢过程的故障。
Steel production process is a typical high temperature, discrete and continuous mixing process of physical and chemical changes, with multi-factor, multi-process, multi-position, strong coupling and nonlinear characteristics. High energy consumption is the main problem in the process of steel smelting production. Energy conservation and consumption is conducive not only to reduce production costs, but also to achieve a low-carbon processing purpose. Prediction of energy consumption in iron and steel production logistics can provide support for the iron and steel enterprises to formulate an energy overall plan; In the process of steel production, rational and efficient production scheduling has an extremely important significance for reducing energy and material consumption, improving product quality, accelerating the production cycle and reducing production costs; In addition, it plays an important role to online testing, failures detecting, avoid wasting and save energy and material consumption cost in the process of steel production. This dissertation covers research on prediction of logistics energy consumption in the process of steel production, scheduling optimization in production process, failures prediction in production process and so on. Mainly including the following three aspects:
(1) For the issues, such as it is difficult to establish energy forecasting model and prediction accuracy is low, this dissertation proposes a model of steel production logistics and energy prediction based on ant colony optimization wavelet neural network. At first, we analyze steel production process and the factors that impact production energy, determine the input parameters constitute feature space. Then reconstruct feature space using wavelet transform, model energy consumption prediction with neural network model. Finally optimize the solution process using ant colony algorithm. The energy consumption prediction experiments in iron-making, steel-making and rolling process show that the proposed method has better universality, at the same time it improves the prediction accuracy and provide guidance for the steel enterprises understanding energy needs in advance.
(2) For the problems that in the process of steel production, there exists multi-objective, multi-constrained and uncertainty in key process of steelmaking continuous casting, we propose a scheduling optimization algorithm based on genetic particle swarm optimization in steel-making and continuous casting process. Firstly, under the constraint condition of steel-making and continuous casting production process, establish scheduling optimization model to minimize the processing time of the optimization target with this understanding of energy saving, reliable quality and production ordered. Then, utilize the characteristics of fast convergence of particle swarm and global search ability of genetic algorithm to optimize the design and solving parameters. And finally, establish optimal scheduling model. Experimental results indicate that the proposed algorithm is an efficient scheduling optimization method in steel-making and continuous casting production, which is capable of compiling the executable steel-making operation plan to achieve continuous casting. And it can also reduce the loss which was made by the oven waiting time between the various processes; reduce the processing flow time, to reduce energy costs.
In order to solve the difficult problems of failures detection or diagnosis in rolling process, we propose a online failure detection model in rolling steel production process based on multi-core learning theory. First, for learning samples, build a nuclear principal component analysis and support vector data description model. And then, preliminary identify the rolling process based on T2, Q statistic and data domain description envelope. Finally, make fine classification for the first results based on multi-core minimum secondary support vector prediction model, and identify the failure categories. We utilize the above model to do experiments for rolling mill furnace failures and testing unit failures, and the results show that the method can effectively detect failures in the process of rolling steel production.
引文
[1]陆钟武,孟庆生.我国钢铁工业能耗预测[J].钢铁,1997,32(5)：69-74.
[2]谢安国,陆钟武.用灰色系统对吨钢综合能耗进行分析及预测[A].1997中国钢铁年会论文集(上),1997.
[3]冯朝辉,张华,王艳红.烧结工序能耗预测与优化研究[J].烧结球团,2012,37(006)：13-17.
[4]杨宏韬,张德江,李秀兰,等.遗传神经网络能耗预测模型在钢铁企业中的应用[J].长春工业大学学报：自然科学版,2007,28(1)：186-189.
[5]张加云,张德江,李新胜.遗传小波神经网络在钢铁企业能耗预测中的应用[J].冶金自动化,2009,33(z1).
[6]Zhao H, Liu R, Zhao Z, et al. Analysis of Energy Consumption Prediction Model Based on Genetic Algorithm and Wavelet Neural Network [C]. Intelligent Systems and Applications (ISA),2011 3rd International Workshop on. IEEE,2011:1-4.
[7]陈光,刘文涛.广义回归神经网络在高炉炼铁能耗预测中的应用[J].冶金能源.2013,32(4)：15-18.
[8]潘瑶,李莉.基于改进型偏最小二乘法的高炉炼铁工序能耗预测方法[J].计算机应用,2012,32(A02)：51-53.
[9]周继程,张春霞,丁毅,等.马钢高炉炼铁工序能耗预测模型[J].钢铁研究学报,2011,23(002)：30-34.
[10]Weil Z, Xian-huil L I U, YiZ D, et al基于支持向量回归的多时间序列自回归方法[J].计算机应用,2012,12(32)：9.
[11]唐文武,鄂加强.轧钢加热炉能耗模糊变权重组合预测[J].工业炉,2005,4：011.
[12]李文兵,纪扬,李华德.基于面向对象的连铸生产过程仿真建模和实现[J],系统仿真学报,2007,19(18)：4288-4291.
[13]Tang L, Liu J, Rong A, et al. A review of planning and scheduling systems and methods for integrated steel production [J]. European Journal of Operational Research, 2001,133(1):1-20.
[14]俞胜平,王秀英,郑秉霖,等.基于变约束规划模型的炼钢连铸动态调度[J].控制理论与应用,2009,26(7)：771-776.
[15]郑忠,徐乐,高小强.基于元胞自动机的车间天车调度仿真模型[J].系统工程理论与实践,2008,28(2)：137-142.
[16]唐秋华,陈伟明,蒋国璋,等.基于JIT的炼钢-连铸生产调度模型研究[J].武汉科技大学学报(自然科学版),2008,1：017.
[17]朱道飞.钢厂生产作业计划与调度的自组织优化方法研究[D].重庆大学博士论文,2008.
[18]庞新富,俞胜平,张志宇,等.炼钢-连铸生产优化重调度方法[J].系统工程学报,2010,25(1)：98-103.
[19]刘小华,林杰,邓可.基于遗传粒子群混合的可重入生产调度优化[J].同济大学学报(自然科学版),2011,5(019)：726-730,772.
[20]俞胜平,庞新富,柴天佑,等.加工时间不确定的炼钢连铸生产调度问题研究[J].控制与决策,2009,24(10)：1467-1472.
[21]Tang L, Luh P B, Liu J, et al. Steel-making process scheduling using Lagrangian relaxation[J]. International Journal of Production Research,2002,40(1):55-70.
[22]蒋少华,桂卫华,阳春华,等.基于核主元分析与支持向量机的监控诊断方法及其应用[J].中南大学学报：自然科学版,2009,40(5)：1323-1328.
[23]张晓龙,尧世文,胡建杭,等.基于CSKPCA-SLSSVM的艾萨炉故障监测研究[J].计算机与应用化学,2012,29(9)：1079-1084.
[24]何学文,赵海鸣.支持向量机及其在机械故障诊断中的应用[J].中南大学学报：自然科学版,2005,36(1)：97-101.
[25]彭涛,杨慧斌,李健宝,等.基于核主元分析的滚动轴承故障混合域特征提取方法[J].中南大学学报(自然科学版),2011,42(11)：3384-3391.
[26]王安娜,吴洁,张丽娜,等.一种基于动态剪枝二叉树SVMs的高炉故障诊断新方法[J].仪器仪表学报,2007,12(12)：2147-2151.
[27]梁军.轧钢加热炉故障检测与诊断方法研究[J].钢铁,2003,38(9)：56-61.
[28]张飞,郭强,申屠南凯.基于PLS的热连轧过程故障检测与诊断[J].轧钢,2008,25(6)：45.
[29]王维兴,李忠.高炉炼铁能耗与节能分析[J].中国钢铁业,2007,2：16-18.
[30]陈冠军.炼铁系统能耗分析及节能[J].冶金能源,2010(004)：11-14.
[31]陈光,蔡九菊,于庆波,等.钢铁企业物流对能耗影响的分析[J].东北大学学报(自然科学版),2002,5.
[32]周渝生,曹传根,甘菲芳.高炉长寿技术的最新进展[J].钢铁,2003,38(11)：16-21.
[33]高炉设计：炼铁工艺设计理论与实践[M].冶金工业出版社,2007.
[34]Dawson P R. Recent developments in iron ore sintering. I. Introduction[J]. Ironmaking and Steelmaking(UK),1993,20(2):135-136.
[35]Numao M, Morishita S I. Cooperative scheduling and its application to steelmaking processes[J]. Industrial Electronics, IEEE Transactions on,1991,38(2):150-155.
[36]Ryu K R, Lim O K, Lee H K. The advanced production and product scheduling control system at the Kwangyang Works [C].Proceedings of the International Conference on Computerized Production Control in Steel Plants, Korea.1993:299-308.
[37]陆钟武,蔡九菊,于庆波,等.钢铁生产流程的物流对能耗的影响[J].金属学报,2000,36(4)：370-378.
[38]陆钟武,戴铁军.钢铁生产流程中物流对能耗和铁耗的影响[J].钢铁.2005,40(4)：1-7.
[39]Fruehan R J, Fortini O, Paxton H W, et al. Theoretical Minimum Energies to Produce Steel for Selected Conditions [R]. Carnegie Mellon University Pittsburgh, Pennsylvania,2000.2-9.
[40]仇晓磊,孟庆玉,洪新.钢铁生产长流程工序能耗数学模型研究[J].冶金能源,2007,26(3)：3-6.
[41]王建军,蔡九菊,张琦,等.钢铁企业能量流模型化研究[J].中国冶金,2006,16(5)：48-52.
[42]王建军.钢铁企业物质流,能量流及其相互关系研究与应用[D]东北大学,2007.
[43]陈光,顾明言.利用神经网络技术预测轧钢厂的工序能耗[J].西安交通大学学报,2001,12：1319-1320.
[44]殷瑞钰.冶金流程工程学[M].北京：冶金工业出版社,2004,350-355
[45]Kirschen M, Risonarta V, Pfeifer H. Energy Efficiency and the Influence of Gas Burners to the Energy Related Carbon Dioxide Emission of Electric Arc Furnaces in Steel Industry [J]. Energy,2009,34(9):1065-1072.
[46]刘青.现代长材型转炉炼钢厂的模式优化研究[D].北京：北京科技大学,2002.72-103.
[47]Ferretti I, Zanoni S, Zavanella L. Energy Efficiency in a Steel Plant Using Optimization simulation [A].EMSS Organization Committee.The 20th European Modeling and Simulation Symposium (EMSS) [C]. Genoa:DIP
[48]Sakamoto Y, Tonooka Y, Yanagisawa Y. Estimation of Energy Consumption for Each Process in the Japanese Steel Industry:A Process Analysis [J].Energy Convers. Manage.1999,40(11):1129-1140.
[49]张娣.能耗指标影响因素分析与对策[J].上海节能,2009,(1)：20-24.
50]Andersen J P, Hyman B. Energy and Material Flow Models for the US Steel Industry [J]. Energy,2001,26(2):137-159.
[51]孙延奎,小波分析及其应用,北京：机械工业出版社,2005：1-8.
[52]Burrus C S, Gopinath R A, Guo H, et al. Introduction to wavelets and wavelet transforms:a primer[M]. Upper Saddle River:Prentice hall,1998.
[53]Mallat S.A theory for multiresolution signal decomposition:the wavelet representation, IEEE Trans. Pattern Anal., Machine Intell.,1989,11(7):674-693.
[54]Torrence C, Compo G P. A practical guide to wavelet analysis[J]. Bulletin of the American Meteorological society,1998,79(1):61-78.
[55]Sheng Y, Roberge D, Szu H H. Optical wavelet transform[J]. Optical Engineering,1992, 31(9):1840-1845.
[56]Misiti M, Misiti Y, Oppenheim G, et al. Wavelet toolbox[J]. The MathWorks Inc., Natick,MA,1996.
57] Zhang J, Walter G G, Miao Y, et al. Wavelet neural networks for function learning[J]. Signal Processing, IEEE Transactions on,1995,43(6):1485-1497.
58] Chen Y, Yang B, Dong J. Time-series prediction using a local linear wavelet neural network[J]. Neurocomputing,2006,69(4):449-465.
[59]Dorigo M, Di Caro G, Gambardella L M. Ant algorithms for discrete optimization [J]. Artificial life,1999,5(2):137-172.
[60]Mullen R J, Monekosso D, Barman S, et al. A review of ant algorithms [J]. Expert Systems with Applications,2009,36(6):9608-9617.
[61]张兆军,冯祖仁,任志刚.采用序优化的改进蚁群算法[J].西安交通大学学报,2010,44(2)：15-19,30.
[62]张寿荣.炼铁系统节能-我国钢铁工业21世纪技术进步的重点[J].钢铁,2005,40(5)：1-4.
[63]Kumar V, Kumar S, Tiwari M K, et al. Auction-based approach to resolve the scheduling problem in the steel making process[J]. International journal of production research,2006,44(8):1503-1522.
[64]Zhu D F, Wang H, Wang S B, et al. Hybrid Genetic Algorithm-Based Production Planning for Steel-Making and Continuous Casting Process[J]. Advanced Materials Research,2012,383:1677-1683.
[65]Pacciarelli D, Pranzo M. Production scheduling in a steelmaking-continuous casting plant[J]. Computers & chemical engineering,2004,28(12):2823-2835.
[66]Tang L, Liu P. Minimizing makespan in a two-machine flowshop scheduling with batching and release time[J]. Mathematical and Computer Modelling,2009,49(5): 1071-1077.
[67]Missbauer H, Hauber W, Stadler W. A scheduling system for the steelmaking-continuous casting process. A case study from the steel-making industry [J]. International Journal of Production Research,2009,47(15):4147-4172.
[68]宁树实,王伟,刘全利.钢铁生产中的加热炉优化调度算法研究[J].控制与决策,2006,21(10)：1138-1142.
[69]Jacobs T L, Wright J R, Cobbs A E. Optimal inter-process steel production scheduling[J]. Computers & operations research,1988,15(6):497-507.
[70]Zarandi M H F, Gamasaee R. Type-2 fuzzy hybrid expert system for prediction of tardiness in scheduling of steel continuous casting process[J]. Soft Computing,2012, 16(8):1287-1302.
[71]Fehnker A. Scheduling a steel plant with timed automata[C]. Real-Time Computing Systems and Applications,1999. RTCSA'99.Sixth International Conference on. IEEE, 1999:280-286.
[72]Zhou Y L. GAP-Like Scheduling Model of Byproduct Gas in Iron and Steel Process[J]. Applied Mechanics and Materials,2012,217:505-510.
[73]周永良,李铁克.面向节能降耗的炼钢-连铸生产调度模型及求解[J].冶金自动化,2012,36(5)：20-23.
[74]Paul R J, Chanev T S. Simulation optimisation using a genetic algorithm [J]. Simulation Practice and Theory,1998,6(6):601-611.
[75]Ueno N, Nakagawa Y, Okuno Y, et al. Steel product transportation and storage simulation:a combined simulation/optimization approach[C].Proceedings of the 20th conference on Winter simulation. ACM,1988:678-683.
[76]Chick S, Sanchez P J. A SIMULATION STUDY ON SAMPLING AND SELECTING UNDER FIXED COMPUTING BUDGET[J].
[77]Klemmt A, Horn S, Weigert G, et al. Simulation-based optimization vs. mathematical programming:A hybrid approach for optimizing scheduling problems[J]. Robotics and Computer-Integrated Manufacturing,2009,25(6):917-925.
[78]伍乃骐,白丽平.炼油生产计划和调度优化的研究[J].计算机集成制造系统,2005,11(001)：90-96
[79]王国新,宁汝新,王爱民.基于仿真的生产调度优化技术研究[J].计算机集成制造系统,2007,13(7)：1419-1427.
[80]李铁克,苏志雄.炼钢连铸生产调度问题的两阶段遗传算法[J].中国管理科学,2009,17(5)：68-74.
[81]汪红兵,徐安军,姚琳,等.应用改进遗传算法求解炼钢连铸生产调度问题[J].北京科技大学学报,2010,9：022.
[82]郑忠,朱道飞,高小强.钢厂炼钢连铸生产调度及重计划方法[J].重庆大学学报,2008,(07).114-118
[83]Tang L, Luh P B, Liu J, et al. Steel-making process scheduling using Lagrangian relaxation[J]. International Journal of Production Research,2002,40(1):55-70.
[84]Pacciarelli D, Pranzo M. Production scheduling in a steelmaking-continuous casting plant[J]. Computers & chemical engineering,2004,28(12):2823-2835.
[85]俞胜平,柴天佑,郑秉霖.炼钢连铸混合智能优化调度方法及应用[J].系统工程学报,2010,25(3)：379-386.
[86]Adenmeso Diaz, Luis Sancho, Ramon Garcia, et al. A dynamic scheduling and control system in an Ensidesa Steel Plant[J]. Interfaces,21(5):53-62.
[87]唐立新,杨自厚,王梦光.炼钢-连铸生产的计划与调度结构[J].东北大学学报：自然科学版,1996,17(6)：664-667.
[88]汪红兵,徐安军,姚琳,等.应用改进遗传算法求解炼钢连铸生产调度问题[J].北京科技大学学报,2010,9：022.
[89]李铁克,苏志雄.炼钢连铸生产调度问题的两阶段遗传算法[J].中国管理科学,2009,17(5)：68-74.
[90]Eberhart R, Kennedy J. A new optimizer using particle swarm theory[C].Micro Machine and Human Science,1995. MHS'95., Proceedings of the Sixth International Symposium on. IEEE,1995:39-43.
[91]Parsopoulos K E, Vrahatis M N. Particle swarm optimizer in noisy and continuously changing environments [J]. methods,2001,5(6):23.
[92]Parsopoulos K E, Vrahatis M N. Particle swarm optimization method for constrained optimization problems [J]. Intelligent Technologies-Theory and Application:New Trends in Intelligent Technologies,2002,76:214-220.
[93]Coello C A C. A comprehensive survey of evolutionary-based multiobjective optimization techniques [J]. Knowledge and Information systems,1999,1(3):129-156.
[94]SVANTE W. Principal component analysis [J]. Chemometrics and Intelligent Laboratory System,1987,2(1-3):37-52.
[95]杨帆,唐朝晖,桂卫华.PCA过程检测方法再密闭鼓风炉铅锌熔炼中的应用[J].计 算机测量与控制,2007,15(90)：1169-1171.
[96]梁军.轧钢加热炉故障检测与诊断方法研究[J].钢铁,2003,38(9)：56-61.
[97]张飞,郭强,申屠南凯.基于PLS的热连轧过程故障检测与诊断[J].轧钢,2008,25(6)：45.
[98]汤健,赵立杰,岳恒.基于多元数据特征融合的球磨机负荷软测量.浙江大学学报,2010,44(7)：1406-1413.
[99]蒋少华,桂卫华,阳春华.基于核主元分析与支持向量机的监控诊断方法及其应用[J].中南大学学报(自然科学版),2009,40(5)：1323-1328.
[100]LEE J M, YOO C K, CHOI S W. Nonlinear process monitoring using kernel principal component analysis[J]. Chemical Engineering Science,2004,59(1):223-234.
[101]汪洪桥,孙富春,蔡艳宁,等.多核学习方法[J].自动化学报,2010,36(8)：1037-1050.
[102]赵会平,陆宁云,姜斌.轧钢过程故障诊断研究现状及发展趋势[J].轧钢,2011,1：017.
[103]刘春林,张永生.韶钢六轧厂粗轧机组故障分析[J].南方冶金,1999,109(8)：13-17.
[104]刘铁男,张晓芳.本钢连轧厂加热炉汽化冷却系统故障的诊断及治理措施[J].本钢技术,2000,(6)：2-8.
[105]徐志栋.冶金行业精轧设备润滑故障分析与改进[J].合成润滑材料,2001,28(1)：28-31.
[106]周明雄.凌士德,姜晓峰.中轧托架故障原因及设计改进[J].浙江冶金,2002,(3)：24-25.
[107]方涌.精轧单机运行的故障分析及处理[J].冶金丛刊,2004,151(3)：26-27.
[108]毛卫东.热轧带钢厂精轧支撑辊轴承故障分析及对策[J].鄂钢技术,2007,(1)：10-12.
[109]Da Wei Gu, Fu Wah Poon. A Robust Fauh-detection Approach with Application in a Rolling-mill Process[J]. IEEE Transactions on Control Systems Technology,2007, 11(3):408-414.
[110]Theilliol D, Mahfouf M, Sauter D, et al. Actuator/sensor Fault Diagnosis for an Experimental Hot Rolling Mill-ACase Study[A]. Reprints of 12th IFAC Symposium on Automation in Mining. Mineral and Metal Processing(IFACMMM)[c]. Qu6bec City, 2007.
[111]谭树彬.轧机厚控系统状态监测与故障诊断的研究与应用[D].沈阳：东北大学,2006.
[112]王芳,董宏.轧机垂直振动的研究现状与展望[J].轧钢,2008.25(3)：45-47.
[113]李小雁,张文斌.基于振动监测的设备故障诊断技术在大型轧钢机械上的应用[J].冶金丛刊,2008,178(6)：24-29.
[114]范胜标.珠钢热连轧机振动的分析及处理[J].轧钢,2005,22(6)：49-51.
[115]罗忠辉.大型薄板坯热连轧机异常振动与故障监测研究[D].广州：广州工业大学,2005.
[116]张芮.热连轧主传动设备状态监测与故障诊断系统的设计及应用[J].冶金内动化,2008.32(3)：65-68.
[117]晁代坤,贾庆勇,孟宪伟.安钢高线设备故障诊断系统及应用[J].轧钢,2005,22(3)：65-68.
[118]谷立臣,张超,段志善.中板轧机性能测试及多源诊断信息获取研究[J].重型机械,2005,6(3)：13-16.
[119]高立新,张建宁,崔玲丽,等.高线轧机典型故障的时频特征分析[J].钢铁,2005,40(9)：4i-44.
[120]Zhong J,Wang Z M,Wang Q,et al. An Intelligent Monitoting and Fault Detection and Diagnosis System in Steel Rolling Mill[A]. In Proceedings of the IEEE International Conference on Industrial Technology[c]. Shanghail 1996.
121]汪曙峰.棒材精轧线故障诊断专家系统[D].武汉：武汉科技大学,2007.
[122]Gu J,Rui Y,Jiang x M,et al. Fault Diagnosis for HighSpeed Rod-rolling Mill Using Neural Networks[A]. In Proceedings of WMSCI[C]. Orlando:USA,Ritzo,2006.
[123]董敏,刘才,李国友.等.轧机液压AGC系统基于神经网络的传感器故障诊断技术[J].钢铁,2005,40(5)：45-48,
[124]汪洪桥,孙富春,蔡艳宁,等.多核学习方法[J].自动化学报,2010,36(8)：1037-1050.
[125]刘金浩,王豫,董亮等.W03基材料研究进展[J],中国钨业,2005,20(5)：17-21.
[126]Kramer M A. Nonlinear principal component analysis using auto associative neural networks[J], AIChE Journal,1991,37:233-243.
[127]Tax D M J, Duin R P W. Support vector data description [J]. Machine Learning,2004, 54(1):45-66.
[128]Vapnik V N. The Nature of Statistical Learning Theory [M], N Y:Springer-Verlag, 1995.
[129]王坤,杜文莉,钱锋.基于小波核聚类的非高斯过程故障检测方法[J].化工学报,2011,62(002)：427-432.
[130]Zien A, Ong C S. Multiclass multiple kernel learning[C].Proceedings of the 24th international conference on Machine learning. ACM,2007:1191-1198.
[2]谢安国,陆钟武.用灰色系统对吨钢综合能耗进行分析及预测[A].1997中国钢铁年会论文集(上),1997.
[3]冯朝辉,张华,王艳红.烧结工序能耗预测与优化研究[J].烧结球团,2012,37(006)：13-17.
[4]杨宏韬,张德江,李秀兰,等.遗传神经网络能耗预测模型在钢铁企业中的应用[J].长春工业大学学报：自然科学版,2007,28(1)：186-189.
[5]张加云,张德江,李新胜.遗传小波神经网络在钢铁企业能耗预测中的应用[J].冶金自动化,2009,33(z1).
[6]Zhao H, Liu R, Zhao Z, et al. Analysis of Energy Consumption Prediction Model Based on Genetic Algorithm and Wavelet Neural Network [C]. Intelligent Systems and Applications (ISA),2011 3rd International Workshop on. IEEE,2011:1-4.
[7]陈光,刘文涛.广义回归神经网络在高炉炼铁能耗预测中的应用[J].冶金能源.2013,32(4)：15-18.
[8]潘瑶,李莉.基于改进型偏最小二乘法的高炉炼铁工序能耗预测方法[J].计算机应用,2012,32(A02)：51-53.
[9]周继程,张春霞,丁毅,等.马钢高炉炼铁工序能耗预测模型[J].钢铁研究学报,2011,23(002)：30-34.
[10]Weil Z, Xian-huil L I U, YiZ D, et al基于支持向量回归的多时间序列自回归方法[J].计算机应用,2012,12(32)：9.
[11]唐文武,鄂加强.轧钢加热炉能耗模糊变权重组合预测[J].工业炉,2005,4：011.
[12]李文兵,纪扬,李华德.基于面向对象的连铸生产过程仿真建模和实现[J],系统仿真学报,2007,19(18)：4288-4291.
[13]Tang L, Liu J, Rong A, et al. A review of planning and scheduling systems and methods for integrated steel production [J]. European Journal of Operational Research, 2001,133(1):1-20.
[14]俞胜平,王秀英,郑秉霖,等.基于变约束规划模型的炼钢连铸动态调度[J].控制理论与应用,2009,26(7)：771-776.
[15]郑忠,徐乐,高小强.基于元胞自动机的车间天车调度仿真模型[J].系统工程理论与实践,2008,28(2)：137-142.
[16]唐秋华,陈伟明,蒋国璋,等.基于JIT的炼钢-连铸生产调度模型研究[J].武汉科技大学学报(自然科学版),2008,1：017.
[17]朱道飞.钢厂生产作业计划与调度的自组织优化方法研究[D].重庆大学博士论文,2008.
[18]庞新富,俞胜平,张志宇,等.炼钢-连铸生产优化重调度方法[J].系统工程学报,2010,25(1)：98-103.
[19]刘小华,林杰,邓可.基于遗传粒子群混合的可重入生产调度优化[J].同济大学学报(自然科学版),2011,5(019)：726-730,772.
[20]俞胜平,庞新富,柴天佑,等.加工时间不确定的炼钢连铸生产调度问题研究[J].控制与决策,2009,24(10)：1467-1472.
[21]Tang L, Luh P B, Liu J, et al. Steel-making process scheduling using Lagrangian relaxation[J]. International Journal of Production Research,2002,40(1):55-70.
[22]蒋少华,桂卫华,阳春华,等.基于核主元分析与支持向量机的监控诊断方法及其应用[J].中南大学学报：自然科学版,2009,40(5)：1323-1328.
[23]张晓龙,尧世文,胡建杭,等.基于CSKPCA-SLSSVM的艾萨炉故障监测研究[J].计算机与应用化学,2012,29(9)：1079-1084.
[24]何学文,赵海鸣.支持向量机及其在机械故障诊断中的应用[J].中南大学学报：自然科学版,2005,36(1)：97-101.
[25]彭涛,杨慧斌,李健宝,等.基于核主元分析的滚动轴承故障混合域特征提取方法[J].中南大学学报(自然科学版),2011,42(11)：3384-3391.
[26]王安娜,吴洁,张丽娜,等.一种基于动态剪枝二叉树SVMs的高炉故障诊断新方法[J].仪器仪表学报,2007,12(12)：2147-2151.
[27]梁军.轧钢加热炉故障检测与诊断方法研究[J].钢铁,2003,38(9)：56-61.
[28]张飞,郭强,申屠南凯.基于PLS的热连轧过程故障检测与诊断[J].轧钢,2008,25(6)：45.
[29]王维兴,李忠.高炉炼铁能耗与节能分析[J].中国钢铁业,2007,2：16-18.
[30]陈冠军.炼铁系统能耗分析及节能[J].冶金能源,2010(004)：11-14.
[31]陈光,蔡九菊,于庆波,等.钢铁企业物流对能耗影响的分析[J].东北大学学报(自然科学版),2002,5.
[32]周渝生,曹传根,甘菲芳.高炉长寿技术的最新进展[J].钢铁,2003,38(11)：16-21.
[33]高炉设计：炼铁工艺设计理论与实践[M].冶金工业出版社,2007.
[34]Dawson P R. Recent developments in iron ore sintering. I. Introduction[J]. Ironmaking and Steelmaking(UK),1993,20(2):135-136.
[35]Numao M, Morishita S I. Cooperative scheduling and its application to steelmaking processes[J]. Industrial Electronics, IEEE Transactions on,1991,38(2):150-155.
[36]Ryu K R, Lim O K, Lee H K. The advanced production and product scheduling control system at the Kwangyang Works [C].Proceedings of the International Conference on Computerized Production Control in Steel Plants, Korea.1993:299-308.
[37]陆钟武,蔡九菊,于庆波,等.钢铁生产流程的物流对能耗的影响[J].金属学报,2000,36(4)：370-378.
[38]陆钟武,戴铁军.钢铁生产流程中物流对能耗和铁耗的影响[J].钢铁.2005,40(4)：1-7.
[39]Fruehan R J, Fortini O, Paxton H W, et al. Theoretical Minimum Energies to Produce Steel for Selected Conditions [R]. Carnegie Mellon University Pittsburgh, Pennsylvania,2000.2-9.
[40]仇晓磊,孟庆玉,洪新.钢铁生产长流程工序能耗数学模型研究[J].冶金能源,2007,26(3)：3-6.
[41]王建军,蔡九菊,张琦,等.钢铁企业能量流模型化研究[J].中国冶金,2006,16(5)：48-52.
[42]王建军.钢铁企业物质流,能量流及其相互关系研究与应用[D]东北大学,2007.
[43]陈光,顾明言.利用神经网络技术预测轧钢厂的工序能耗[J].西安交通大学学报,2001,12：1319-1320.
[44]殷瑞钰.冶金流程工程学[M].北京：冶金工业出版社,2004,350-355
[45]Kirschen M, Risonarta V, Pfeifer H. Energy Efficiency and the Influence of Gas Burners to the Energy Related Carbon Dioxide Emission of Electric Arc Furnaces in Steel Industry [J]. Energy,2009,34(9):1065-1072.
[46]刘青.现代长材型转炉炼钢厂的模式优化研究[D].北京：北京科技大学,2002.72-103.
[47]Ferretti I, Zanoni S, Zavanella L. Energy Efficiency in a Steel Plant Using Optimization simulation [A].EMSS Organization Committee.The 20th European Modeling and Simulation Symposium (EMSS) [C]. Genoa:DIP
[48]Sakamoto Y, Tonooka Y, Yanagisawa Y. Estimation of Energy Consumption for Each Process in the Japanese Steel Industry:A Process Analysis [J].Energy Convers. Manage.1999,40(11):1129-1140.
[49]张娣.能耗指标影响因素分析与对策[J].上海节能,2009,(1)：20-24.
50]Andersen J P, Hyman B. Energy and Material Flow Models for the US Steel Industry [J]. Energy,2001,26(2):137-159.
[51]孙延奎,小波分析及其应用,北京：机械工业出版社,2005：1-8.
[52]Burrus C S, Gopinath R A, Guo H, et al. Introduction to wavelets and wavelet transforms:a primer[M]. Upper Saddle River:Prentice hall,1998.
[53]Mallat S.A theory for multiresolution signal decomposition:the wavelet representation, IEEE Trans. Pattern Anal., Machine Intell.,1989,11(7):674-693.
[54]Torrence C, Compo G P. A practical guide to wavelet analysis[J]. Bulletin of the American Meteorological society,1998,79(1):61-78.
[55]Sheng Y, Roberge D, Szu H H. Optical wavelet transform[J]. Optical Engineering,1992, 31(9):1840-1845.
[56]Misiti M, Misiti Y, Oppenheim G, et al. Wavelet toolbox[J]. The MathWorks Inc., Natick,MA,1996.
57] Zhang J, Walter G G, Miao Y, et al. Wavelet neural networks for function learning[J]. Signal Processing, IEEE Transactions on,1995,43(6):1485-1497.
58] Chen Y, Yang B, Dong J. Time-series prediction using a local linear wavelet neural network[J]. Neurocomputing,2006,69(4):449-465.
[59]Dorigo M, Di Caro G, Gambardella L M. Ant algorithms for discrete optimization [J]. Artificial life,1999,5(2):137-172.
[60]Mullen R J, Monekosso D, Barman S, et al. A review of ant algorithms [J]. Expert Systems with Applications,2009,36(6):9608-9617.
[61]张兆军,冯祖仁,任志刚.采用序优化的改进蚁群算法[J].西安交通大学学报,2010,44(2)：15-19,30.
[62]张寿荣.炼铁系统节能-我国钢铁工业21世纪技术进步的重点[J].钢铁,2005,40(5)：1-4.
[63]Kumar V, Kumar S, Tiwari M K, et al. Auction-based approach to resolve the scheduling problem in the steel making process[J]. International journal of production research,2006,44(8):1503-1522.
[64]Zhu D F, Wang H, Wang S B, et al. Hybrid Genetic Algorithm-Based Production Planning for Steel-Making and Continuous Casting Process[J]. Advanced Materials Research,2012,383:1677-1683.
[65]Pacciarelli D, Pranzo M. Production scheduling in a steelmaking-continuous casting plant[J]. Computers & chemical engineering,2004,28(12):2823-2835.
[66]Tang L, Liu P. Minimizing makespan in a two-machine flowshop scheduling with batching and release time[J]. Mathematical and Computer Modelling,2009,49(5): 1071-1077.
[67]Missbauer H, Hauber W, Stadler W. A scheduling system for the steelmaking-continuous casting process. A case study from the steel-making industry [J]. International Journal of Production Research,2009,47(15):4147-4172.
[68]宁树实,王伟,刘全利.钢铁生产中的加热炉优化调度算法研究[J].控制与决策,2006,21(10)：1138-1142.
[69]Jacobs T L, Wright J R, Cobbs A E. Optimal inter-process steel production scheduling[J]. Computers & operations research,1988,15(6):497-507.
[70]Zarandi M H F, Gamasaee R. Type-2 fuzzy hybrid expert system for prediction of tardiness in scheduling of steel continuous casting process[J]. Soft Computing,2012, 16(8):1287-1302.
[71]Fehnker A. Scheduling a steel plant with timed automata[C]. Real-Time Computing Systems and Applications,1999. RTCSA'99.Sixth International Conference on. IEEE, 1999:280-286.
[72]Zhou Y L. GAP-Like Scheduling Model of Byproduct Gas in Iron and Steel Process[J]. Applied Mechanics and Materials,2012,217:505-510.
[73]周永良,李铁克.面向节能降耗的炼钢-连铸生产调度模型及求解[J].冶金自动化,2012,36(5)：20-23.
[74]Paul R J, Chanev T S. Simulation optimisation using a genetic algorithm [J]. Simulation Practice and Theory,1998,6(6):601-611.
[75]Ueno N, Nakagawa Y, Okuno Y, et al. Steel product transportation and storage simulation:a combined simulation/optimization approach[C].Proceedings of the 20th conference on Winter simulation. ACM,1988:678-683.
[76]Chick S, Sanchez P J. A SIMULATION STUDY ON SAMPLING AND SELECTING UNDER FIXED COMPUTING BUDGET[J].
[77]Klemmt A, Horn S, Weigert G, et al. Simulation-based optimization vs. mathematical programming:A hybrid approach for optimizing scheduling problems[J]. Robotics and Computer-Integrated Manufacturing,2009,25(6):917-925.
[78]伍乃骐,白丽平.炼油生产计划和调度优化的研究[J].计算机集成制造系统,2005,11(001)：90-96
[79]王国新,宁汝新,王爱民.基于仿真的生产调度优化技术研究[J].计算机集成制造系统,2007,13(7)：1419-1427.
[80]李铁克,苏志雄.炼钢连铸生产调度问题的两阶段遗传算法[J].中国管理科学,2009,17(5)：68-74.
[81]汪红兵,徐安军,姚琳,等.应用改进遗传算法求解炼钢连铸生产调度问题[J].北京科技大学学报,2010,9：022.
[82]郑忠,朱道飞,高小强.钢厂炼钢连铸生产调度及重计划方法[J].重庆大学学报,2008,(07).114-118
[83]Tang L, Luh P B, Liu J, et al. Steel-making process scheduling using Lagrangian relaxation[J]. International Journal of Production Research,2002,40(1):55-70.
[84]Pacciarelli D, Pranzo M. Production scheduling in a steelmaking-continuous casting plant[J]. Computers & chemical engineering,2004,28(12):2823-2835.
[85]俞胜平,柴天佑,郑秉霖.炼钢连铸混合智能优化调度方法及应用[J].系统工程学报,2010,25(3)：379-386.
[86]Adenmeso Diaz, Luis Sancho, Ramon Garcia, et al. A dynamic scheduling and control system in an Ensidesa Steel Plant[J]. Interfaces,21(5):53-62.
[87]唐立新,杨自厚,王梦光.炼钢-连铸生产的计划与调度结构[J].东北大学学报：自然科学版,1996,17(6)：664-667.
[88]汪红兵,徐安军,姚琳,等.应用改进遗传算法求解炼钢连铸生产调度问题[J].北京科技大学学报,2010,9：022.
[89]李铁克,苏志雄.炼钢连铸生产调度问题的两阶段遗传算法[J].中国管理科学,2009,17(5)：68-74.
[90]Eberhart R, Kennedy J. A new optimizer using particle swarm theory[C].Micro Machine and Human Science,1995. MHS'95., Proceedings of the Sixth International Symposium on. IEEE,1995:39-43.
[91]Parsopoulos K E, Vrahatis M N. Particle swarm optimizer in noisy and continuously changing environments [J]. methods,2001,5(6):23.
[92]Parsopoulos K E, Vrahatis M N. Particle swarm optimization method for constrained optimization problems [J]. Intelligent Technologies-Theory and Application:New Trends in Intelligent Technologies,2002,76:214-220.
[93]Coello C A C. A comprehensive survey of evolutionary-based multiobjective optimization techniques [J]. Knowledge and Information systems,1999,1(3):129-156.
[94]SVANTE W. Principal component analysis [J]. Chemometrics and Intelligent Laboratory System,1987,2(1-3):37-52.
[95]杨帆,唐朝晖,桂卫华.PCA过程检测方法再密闭鼓风炉铅锌熔炼中的应用[J].计 算机测量与控制,2007,15(90)：1169-1171.
[96]梁军.轧钢加热炉故障检测与诊断方法研究[J].钢铁,2003,38(9)：56-61.
[97]张飞,郭强,申屠南凯.基于PLS的热连轧过程故障检测与诊断[J].轧钢,2008,25(6)：45.
[98]汤健,赵立杰,岳恒.基于多元数据特征融合的球磨机负荷软测量.浙江大学学报,2010,44(7)：1406-1413.
[99]蒋少华,桂卫华,阳春华.基于核主元分析与支持向量机的监控诊断方法及其应用[J].中南大学学报(自然科学版),2009,40(5)：1323-1328.
[100]LEE J M, YOO C K, CHOI S W. Nonlinear process monitoring using kernel principal component analysis[J]. Chemical Engineering Science,2004,59(1):223-234.
[101]汪洪桥,孙富春,蔡艳宁,等.多核学习方法[J].自动化学报,2010,36(8)：1037-1050.
[102]赵会平,陆宁云,姜斌.轧钢过程故障诊断研究现状及发展趋势[J].轧钢,2011,1：017.
[103]刘春林,张永生.韶钢六轧厂粗轧机组故障分析[J].南方冶金,1999,109(8)：13-17.
[104]刘铁男,张晓芳.本钢连轧厂加热炉汽化冷却系统故障的诊断及治理措施[J].本钢技术,2000,(6)：2-8.
[105]徐志栋.冶金行业精轧设备润滑故障分析与改进[J].合成润滑材料,2001,28(1)：28-31.
[106]周明雄.凌士德,姜晓峰.中轧托架故障原因及设计改进[J].浙江冶金,2002,(3)：24-25.
[107]方涌.精轧单机运行的故障分析及处理[J].冶金丛刊,2004,151(3)：26-27.
[108]毛卫东.热轧带钢厂精轧支撑辊轴承故障分析及对策[J].鄂钢技术,2007,(1)：10-12.
[109]Da Wei Gu, Fu Wah Poon. A Robust Fauh-detection Approach with Application in a Rolling-mill Process[J]. IEEE Transactions on Control Systems Technology,2007, 11(3):408-414.
[110]Theilliol D, Mahfouf M, Sauter D, et al. Actuator/sensor Fault Diagnosis for an Experimental Hot Rolling Mill-ACase Study[A]. Reprints of 12th IFAC Symposium on Automation in Mining. Mineral and Metal Processing(IFACMMM)[c]. Qu6bec City, 2007.
[111]谭树彬.轧机厚控系统状态监测与故障诊断的研究与应用[D].沈阳：东北大学,2006.
[112]王芳,董宏.轧机垂直振动的研究现状与展望[J].轧钢,2008.25(3)：45-47.
[113]李小雁,张文斌.基于振动监测的设备故障诊断技术在大型轧钢机械上的应用[J].冶金丛刊,2008,178(6)：24-29.
[114]范胜标.珠钢热连轧机振动的分析及处理[J].轧钢,2005,22(6)：49-51.
[115]罗忠辉.大型薄板坯热连轧机异常振动与故障监测研究[D].广州：广州工业大学,2005.
[116]张芮.热连轧主传动设备状态监测与故障诊断系统的设计及应用[J].冶金内动化,2008.32(3)：65-68.
[117]晁代坤,贾庆勇,孟宪伟.安钢高线设备故障诊断系统及应用[J].轧钢,2005,22(3)：65-68.
[118]谷立臣,张超,段志善.中板轧机性能测试及多源诊断信息获取研究[J].重型机械,2005,6(3)：13-16.
[119]高立新,张建宁,崔玲丽,等.高线轧机典型故障的时频特征分析[J].钢铁,2005,40(9)：4i-44.
[120]Zhong J,Wang Z M,Wang Q,et al. An Intelligent Monitoting and Fault Detection and Diagnosis System in Steel Rolling Mill[A]. In Proceedings of the IEEE International Conference on Industrial Technology[c]. Shanghail 1996.
121]汪曙峰.棒材精轧线故障诊断专家系统[D].武汉：武汉科技大学,2007.
[122]Gu J,Rui Y,Jiang x M,et al. Fault Diagnosis for HighSpeed Rod-rolling Mill Using Neural Networks[A]. In Proceedings of WMSCI[C]. Orlando:USA,Ritzo,2006.
[123]董敏,刘才,李国友.等.轧机液压AGC系统基于神经网络的传感器故障诊断技术[J].钢铁,2005,40(5)：45-48,
[124]汪洪桥,孙富春,蔡艳宁,等.多核学习方法[J].自动化学报,2010,36(8)：1037-1050.
[125]刘金浩,王豫,董亮等.W03基材料研究进展[J],中国钨业,2005,20(5)：17-21.
[126]Kramer M A. Nonlinear principal component analysis using auto associative neural networks[J], AIChE Journal,1991,37:233-243.
[127]Tax D M J, Duin R P W. Support vector data description [J]. Machine Learning,2004, 54(1):45-66.
[128]Vapnik V N. The Nature of Statistical Learning Theory [M], N Y:Springer-Verlag, 1995.
[129]王坤,杜文莉,钱锋.基于小波核聚类的非高斯过程故障检测方法[J].化工学报,2011,62(002)：427-432.
[130]Zien A, Ong C S. Multiclass multiple kernel learning[C].Proceedings of the 24th international conference on Machine learning. ACM,2007:1191-1198.
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