[1]郑静,熊伟丽.基于互信息的多块k近邻故障监测及诊断[J].智能系统学报,2021,16(4):717-728.[doi:10.11992/tis.202007035]
ZHENG Jing,XIONG Weili.Multiblock k-nearest neighbor fault monitoring and diagnosis based on mutual information[J].CAAI Transactions on Intelligent Systems,2021,16(4):717-728.[doi:10.11992/tis.202007035]
点击复制
ZHENG Jing,XIONG Weili.Multiblock k-nearest neighbor fault monitoring and diagnosis based on mutual information[J].CAAI Transactions on Intelligent Systems,2021,16(4):717-728.[doi:10.11992/tis.202007035]
基于互信息的多块k近邻故障监测及诊断
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
16
期数:
2021年第4期
页码:
717-728
栏目:
学术论文—智能系统
出版日期:
2021-07-05
- Title:
- Multiblock k-nearest neighbor fault monitoring and diagnosis based on mutual information
- Keywords:
- mutual information; multi-block modeling; k-nearest neighbor; process monitoring; fault detection; Bayesian inference; fault diagnosis; Mahalanobis distance
- 分类号:
- TP277
- 摘要:
- 由于传统的k近邻故障监测不考虑过程的局部信息，只建立一个全局模型，因此提出一种基于互信息的多块k近邻故障监测方法。首先，考虑建模数据的非线性和非高斯等特性，基于变量间的互信息进行子块构建；然后，利用k近邻方法对每个子块进行建模与监测，子块中的k近邻模型反映了更多的过程局部特征；最后，将所有子块的监测结果通过贝叶斯推断方法进行融合，并采用基于马氏距离的故障诊断方法辨识故障源。通过对田纳西-伊斯曼过程和高炉炼铁过程中的应用仿真，监测结果表明所提方法的可行性和有效性。
- Abstract:
- The traditional k-nearest neighbor (kNN) fault monitoring does not take into account the process of local information and only builds a global model. Thus, a multi-block kNN fault monitoring algorithm based on mutual information is proposed. First, with the nonlinear and non-Gaussian characteristics of the modeled data taken into consideration, subblocks are constructed based on mutual information between variables. Then, the kNN algorithm is used to model and monitor each subblock, in which the kNN model reflects more local characteristics of the process. Lastly, the monitoring results of all subblocks are fused by the Bayesian inference method, and a fault diagnosis method based on Mahalanobis distance is used to identify the source of faults. Through the application simulation in the Tennessee Eastman process and the blast furnace ironmaking process, the monitoring results show the feasibility and effectiveness of the proposed method.
- 参考文献/References:
-
[1] CHIANG L H, RUSSELL E L, BRAATZ R D. Fault detection and diagnosis in industrial systems[J]. Measurement science and technology, 2001, 12(10): 1745.
[2] GE Zhiqiang, SONG Zhihuan, GAO Furong. Review of recent research on data-based process monitoring[J]. Industrial & engineering chemistry research, 2013, 52(10): 3534-3562.
[3] QIN S J. Statistical process monitoring: basics and beyond[J]. Journal of chemometrics, 2003, 17(8/9): 480-502.
[4] JIANG Qingchao, YAN Xuefeng, ZHAO Weixiang. Fault detection and diagnosis in chemical processes using sensitive principal component analysis[J]. Industrial & engineering chemistry research, 2013, 52(4): 1635-1644.
[5] KRESTA J V, MACGREGOR J F, MARLIN T E. Multivariate statistical monitoring of process operating performance[J]. The Canadian journal of chemical engineering, 1991, 69(1): 35-47.
[6] NOMIKOS P, MACGREGOR J F. Multivariate SPC charts for monitoring batch processes[J]. Technometrics, 1995, 37(1): 41-59.
[7] HE Q P, WANG Jin. Principal component based k-nearest-neighbor rule for semiconductor process fault detection[C]//Proceedings of 2018 American Control Conference. Seattle, USA, 2008: 1606-1611.
[8] HE Q P, WANG Jin. Fault detection using the k-Nearest neighbor rule for semiconductor manufacturing processes[J]. IEEE transactions on semiconductor manufacturing, 2007, 20(4): 345-354.
[9] 陈海彬, 郭金玉, 谢彦红. 基于改进K-means聚类的kNN故障检测研究[J]. 沈阳化工大学学报, 2013, 27(1): 69-73
CHEN Haibin, GUO Jinyu, XIE Yanhong. kNN fault detection based on improved K-means clustering[J]. Journal of Shenyang University of Chemical Technology, 2013, 27(1): 69-73
[10] 魏域琴, 宋丹丹, 翁正新. 基于DPCA-KNN的工业过程故障诊断方法研究[C]//第三十八届中国控制会议论文集(7). 广州, 2019: 14-19.
WEI Yuqin, SONG Dandan, WENG Zhengxin. Research on fault diagnosis method of industrial process based on DPCA-KNN[C]//Proceedings of the 38th China Control Conference. Guangzhou, 2019: 14-19.
[11] 郭小萍, 徐月, 李元. 基于特征空间自适应k近邻工业过程故障检测[J]. 高校化学工程学报, 2019, 33(2): 453-461
GUO Xiaoping, XU Yue, LI Yuan. A feature space adaptive k-nearest neighbor method for industrial fault detection[J]. Journal of chemical engineering of Chinese universities, 2019, 33(2): 453-461
[12] 冯立伟, 张成, 李元, 等. 基于标准距离k近邻的多模态过程故障检测策略[J]. 控制理论与应用, 2019, 36(4): 553-560
FENG Liwei, ZHANG Cheng, LI Yuan, et al. Fault detection strategy of standard-distance-based k nearest neighbor rule in multimode processes[J]. Control theory & applications, 2019, 36(4): 553-560
[13] MACGREGOR J F, JAECKLE C, KIPARISSIDES C, et al. Process monitoring and diagnosis by multiblock PLS methods[J]. AIChE journal, 1994, 40(5): 826-838.
[14] 王振雷, 江伟, 王昕. 基于多块MICA-PCA的全流程过程监控方法[J]. 控制与决策, 2018, 33(2): 269-274
WANG Zhenlei, JIANG Wei, WANG Xin. Plant-wide process monitoring based on multiblock MICA-PCA[J]. Control and decision, 2018, 33(2): 269-274
[15] 石怀涛, 王雨桐, 李颂华, 等. 基于多块相对变换独立主元分析的故障诊断方法[J]. 控制与决策, 2018, 33(11): 2009-2014
SHI Huaitao, WANG Yutong, LI Songhua, et al. Fault diagnosis approach based on relative transformation ICA of multiblock[J]. Control and decision, 2018, 33(11): 2009-2014
[16] GE Zhiqiang, SONG Zhihuan. Distributed PCA model for plant-wide process monitoring[J]. Industrial & engineering chemistry research, 2013, 52(5): 1947-1957.
[17] JIANG Qingchao, YAN Xuefeng. Plant-wide process monitoring based on mutual information-multiblock principal component analysis[J]. ISA transactions, 2014, 53(5): 1516-1527.
[18] HUANG Junping, YAN Xuefeng. Quality relevant and independent two block monitoring based on mutual information and KPCA[J]. IEEE transactions on industrial electronics, 2017, 64(8): 6518-6527.
[19] JIANG Qingchao, YAN Xuefeng. Nonlinear plant-wide process monitoring using MI-spectral clustering and Bayesian inference-based multiblock KPCA[J]. Journal of process control, 2015, 32: 38-50.
[20] 童楚东, 史旭华. 基于互信息的PCA方法及其在过程监测中的应用[J]. 化工学报, 2015, 66(10): 4101-4106
TONG Chudong, SHI Xuhua. Mutual information based PCA algorithm with application in process monitoring[J]. CIESC journal, 2015, 66(10): 4101-4106
[21] 范雪莉, 冯海泓, 原猛. 基于互信息的主成分分析用于声场景分类[J]. 声学技术, 2013, 32(3): 222-227
FAN Xueli, FENG Haihong, YUAN Meng. Mutual information based principal component analysis for acoustic environment classification[J]. Technical acoustics, 2013, 32(3): 222-227
[22] 周红标, 乔俊飞. 基于高维k-近邻互信息的特征选择方法[J]. 智能系统学报, 2017, 12(5): 595-600
ZHOU Hongbiao, QIAO Junfei. Feature selection method based on high dimensional k-nearest neighbors mutual information[J]. CAAI transactions on intelligent systems, 2017, 12(5): 595-600
[23] GE Zhiqiang, SONG Zhihuan. Multimode process monitoring based on Bayesian method[J]. Journal of chemometrics, 2009, 23(12): 636-650.
[24] YIN Shen, DING S X, HAGHANI A, et al. A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process[J]. Journal of process control, 2012, 22(9): 1567-1581.
[25] CHIANG L H, RUSSELL E L, BRAATZ R D. Tennessee Eastman process[M]//CHIANG L H, RUSSELL E L, BRAATZ R D. Fault Detection and Diagnosis in Industrial Systems. London: Springer, 2001: 103-112.
- 相似文献/References:
-
[1]陈伟卿,李冠华,欧宗瑛,等.基于灰度互信息和梯度相似性的医学图像配准及其加速处理[J].智能系统学报,2008,3(6):498.
CHEN Wei-qing,LI Guan-hua,OU Zong-ying,et al.Medical image registration based on grey mutual information and gradient similarity with an accelerated processing method[J].CAAI Transactions on Intelligent Systems,2008,3():498.
[2]李冰寒,高晓利,刘三阳,等.利用互信息学习贝叶斯网络结构[J].智能系统学报,2011,6(1):68.
LI-Binghan,GAO-Xiaoli,LIU-Sanyang,et al.Learning Bayesian network structures based on mutual information[J].CAAI Transactions on Intelligent Systems,2011,6():68.
[3]张昭昭,乔俊飞,杨刚.自适应前馈神经网络结构优化设计[J].智能系统学报,2011,6(4):312.
ZHANG Zhaozhao,QIAO Junfei,YANG Gang.An adaptive algorithm for designingoptimal feedforward neural network architecture[J].CAAI Transactions on Intelligent Systems,2011,6():312.
[4]宋晓丽,刘冀伟,张晓星.分布式视频编码的关键帧提取算法[J].智能系统学报,2011,6(6):539.
SONG Xiaoli,LIU Jiwei,ZHANG Xiaoxing.A key frame selection algorithm for distributed video coding[J].CAAI Transactions on Intelligent Systems,2011,6():539.
[5]周红标,乔俊飞.基于高维k-近邻互信息的特征选择方法[J].智能系统学报,2017,12(5):595.[doi:10.11992/tis.201609020]
ZHOU Hongbiao,QIAO Junfei.Feature selection method based on high dimensional k-nearest neighbors mutual information[J].CAAI Transactions on Intelligent Systems,2017,12():595.[doi:10.11992/tis.201609020]
[6]王贺彬,葛泉波,刘华平,等.面向观测融合和吸引因子的多机器人主动SLAM[J].智能系统学报,2021,16(2):371.[doi:10.11992/tis.202006019]
WANG Hebin,GE Quanbo,LIU Huaping,et al.Multi-robot active SLAM for observation fusion and attractor[J].CAAI Transactions on Intelligent Systems,2021,16():371.[doi:10.11992/tis.202006019]
[7]曲海成,王宇萍,谢梦婷,等.结合亮度感知与密集卷积的红外与可见光图像融合[J].智能系统学报,2022,17(3):643.[doi:10.11992/tis.202104004]
QU Haicheng,WANG Yuping,XIE Mengting,et al.Infrared and visible image fusion combined with brightness perception and dense convolution[J].CAAI Transactions on Intelligent Systems,2022,17():643.[doi:10.11992/tis.202104004]
[8]张智慧,杨燕,张熠玲.面向不完整多视图聚类的深度互信息最大化方法[J].智能系统学报,2023,18(1):12.[doi:10.11992/tis.202203051]
ZHANG Zhihui,YANG Yan,ZHANG Yiling.Deep mutual information maximization method for incomplete multi-view clustering[J].CAAI Transactions on Intelligent Systems,2023,18():12.[doi:10.11992/tis.202203051]
[9]余紫康,董红斌.具有混合策略的樽海鞘群特征选择算法[J].智能系统学报,2024,19(3):757.[doi:10.11992/tis.202209040]
YU Zikang,DONG Hongbin.Salp swarm feature selection algorithm with a hybrid strategy[J].CAAI Transactions on Intelligent Systems,2024,19():757.[doi:10.11992/tis.202209040]
[10]李旭,蔡彪,胡能兵.基于三元互信息的图对比学习方法研究[J].智能系统学报,2024,19(5):1257.[doi:10.11992/tis.202308004]
LI Xu,CAI Biao,HU Nengbing.Research on graph contrastive learning method based on ternary mutual information[J].CAAI Transactions on Intelligent Systems,2024,19():1257.[doi:10.11992/tis.202308004]
备注/Memo
收稿日期:2020-07-22。
基金项目:国家自然科学基金项目(61773182)；国家重点研发计划子课题(2018YFC1603705-03)
作者简介:郑静，硕士研究生，主要研究方向为过程故障检测;熊伟丽，教授，博士生导师，主要研究方向为复杂工业过程建模与监控、智能软测量技术。主持国家自然科学基金面上项目、国家自然科学基金青年项目、江苏省产学研等省部级以上纵向项目等，获授权发明专利 近20项。发表学术论文近百篇.
通讯作者:熊伟丽.E-mail:greenpre@163.com
更新日期/Last Update:
1900-01-01