回转窑烧成带状态混合智能识别方法
Hybrid Recognition Method for Burning Zone Condition of Rotary Kiln
查看参考文献12篇
|
文摘
|
针对氧化铝回转窑过程复杂、长期依赖人工看火操作而造成的生产过程不稳定、产品质量一致性差、能源消耗大等问题,提出了基于烧成带火焰图像特征与关键过程数据融合的烧成带状态自动识别方法,该方法由烧成带火焰图像的分割、特征提取、关键过程数据的融合以及二叉树支持向量机分类器模型组成.工业实验表明,该方法能够较准确地识别烧成带状态,为基于产品质量指标优化的窑温控制器提供决策依据. |
|
其他语种文摘
|
Because of complex conditions of the burning zone, alumina rotary kiln production control has depended on man-watch operation mode for many years, which causes problems of weak coherence of product quality and big depletion of resources. A recognition method for burning zone conditions based on flame images and process data is put forward in this paper, with data fusion techniques and pattern recognition techniques. The method consists of four parts: burning zone flame image segmentation, feature extraction, process key data fusion, and design and parameters selection for classifier model based on the support vector machine. The industrial experiments prove that the burning zone condition can be recognized correctly with the proposed method, which provide decision basis to kiln temperature controller for product quality indices optimization. |
|
来源
|
自动化学报
,2012,38(7):1153-1161 【核心库】
|
|
DOI
|
10.3724/sp.j.1004.2012.01153
|
|
关键词
|
氧化铝回转窑
;
烧成带状态
;
图像处理
;
模式识别
|
|
地址
|
1.
中国科学院沈阳自动化研究所工业信息学研究室, 沈阳, 110016
2.
东北大学信息科学与工程学院, 沈阳, 110819
3.
中国刑事警察学院声像资料检验技术系, 沈阳, 110854
|
|
语种
|
中文 |
|
ISSN
|
0254-4156 |
|
学科
|
冶金工业 |
|
基金
|
国家自然科学基金
;
国家高技术研究发展计划(863计划)
|
|
文献收藏号
|
CSCD:4579394
|
参考文献 共
12
共1页
|
|
1.
袁铸钢. FUZZY控制理论在水泥机立窑煅烧过程的应用.
自动化学报,1999,25(1):25-31
|
被引
3
次
|
|
|
|
|
2.
李树涛. 基于燃烧火焰图象特征的回转窑神经网络控制系统.
自动化学报,2002,28(4):591-595
|
被引
6
次
|
|
|
|
|
3.
刘辉. 基于火焰图像特征与GRNN的转炉吹炼状态识别.
计算机工程与应用,2011,47(26):7-10
|
被引
9
次
|
|
|
|
|
4.
Zhou X J. Pattern-based hybrid intelligent control for rotary kiln process.
Proceedings of the 16th IEEE In-ternational Conference on Control Applications,2007:30-35
|
被引
1
次
|
|
|
|
|
5.
张小刚. 基于图像反馈的回转窑烧结温度智能预测控制.
控制理论与应用,2007,24(6):995-998
|
被引
8
次
|
|
|
|
|
6.
Sadeghian M. Identification, prediction and detec-tion of the process fault in a cement rotary kiln by locally lin-ear neuro-fuzzy technique.
Journal of Process Control,2011,21(2):302-308
|
被引
4
次
|
|
|
|
|
7.
Otsu N. A threshold selection method from gray-level his-tograms.
IEEE Transactions on Systems, Man, and Cyber-netics,1979,9(1):62-66
|
被引
2271
次
|
|
|
|
|
8.
Polat K. Classification of Parkinson0s disease using feature weighting method on the basis of fuzzy C-means clustering.
International Journal of Systems Science,2012,43(4):597-609
|
被引
3
次
|
|
|
|
|
9.
Nounou M N. On-line multiscale filtering of random and gross errors without process models.
AIChE Journal,1999,45(5):1041-1058
|
被引
9
次
|
|
|
|
|
10.
Chang C C.
LIBSVM: a library for support vector machines [Online], available,2010
|
被引
1
次
|
|
|
|
|
11.
Bamesh B D C. Improved content based image retrieval using SMO and SVM classification technique.
European Journal of Scientific Research,2012,69(4):560-564
|
被引
1
次
|
|
|
|
|
12.
Wang J G. A modified incremental support vector machine for regression.
Applied Mechanics and Materials,2012,135/136:63-69
|
被引
2
次
|
|
|
|
|
了解气象卫星更多信息，请访问