基于聚类选择k近邻的LLE算法及故障检测

doi:10.11949/j.issn.0438-1157.20151963

化工学报 ›› 2016, Vol. 67 ›› Issue (3): 925-930.DOI: 10.11949/j.issn.0438-1157.20151963

基于聚类选择k近邻的LLE算法及故障检测

薄翠梅, 韩晓春, 易辉, 李俊

南京工业大学电气工程与控制科学学院, 江苏南京 211816

收稿日期:2015-12-24 修回日期:2016-01-06 出版日期:2016-03-05 发布日期:2016-01-12
通讯作者: 薄翠梅
基金资助:
国家自然科学基金项目(61203020,61503181);江苏省自然科学基金项目(BK20141461,BK20140953)。

Neighborhood selection of LLE based on cluster for fault detection

BO Cuimei, HAN Xiaochun, YI Hui, LI Jun

College of Electrical Engineering and Control Sciences, Nanjing Tech University, Nanjing 211816, Jiangsu, China

Received:2015-12-24 Revised:2016-01-06 Online:2016-03-05 Published:2016-01-12
Contact: 67
Supported by:
supported by the National Natural Science Foundation of China (61203020, 61503181) and the Natural Science Foundation of Jiangsu Province (BK20141461, BK20140953).

摘要/Abstract

摘要：

针对化工过程在多种运行模式下多种流形结构具有不同最优近邻数问题,提出了基于聚类选择k近邻的局部线性嵌入(LLE)过程监控方法。使用LLE算法提取高维数据的低维子流形,通过局部线性回归得到高维数据空间到特征空间的映射矩阵;选择Silhouette指标作为聚类有效性指标评估嵌入空间样本信息的相似性,进而确定最优近邻数,根据映射矩阵构建故障监控统计量及其控制限,进行故障检测。最后将所提算法与其他经典算法应用于TE化工过程对比分析,验证了算法的有效性。

关键词: 局部线性嵌入, 最近邻数, 子流形, 故障检测, 聚类指标

Abstract:

In the process of chemical engineering, multiple manifold structures has different optimal number of nearest neighborhood under various operating modes. Locally linear embedding (LLE) algorithm based on clustering to select the nearest neighborhood is proposed for nonlinear monitoring. LLE algorithm was performed for dimensionality reduction and extract the available information in high-dimensional data. The mapping matrix from data space to feature space was obtained by local linear regression. The Silhouette index was selected as the clustering validity index to estimate the similarity between the embedded sample information, and further determine the optimal number of neighbors. Process monitoring statistics and its control limits were built based on the mapping matrix. Finally, the feasibility and efficiency of the proposed method were illustrated through the Tennessee Eastman process.

Key words: locally linear embedding, the number of nearest neighbor, sub-manifold, fault detection, clustering index

中图分类号:

TP277

薄翠梅, 韩晓春, 易辉, 李俊. 基于聚类选择k近邻的LLE算法及故障检测[J]. 化工学报, 2016, 67(3): 925-930.

BO Cuimei, HAN Xiaochun, YI Hui, LI Jun. Neighborhood selection of LLE based on cluster for fault detection[J]. CIESC Journal, 2016, 67(3): 925-930.

参考文献 20

[1]	王健, 冯健, 韩志艳. 基于流形学习的局部保持PCA算法在故障检测中的应用[J]. 控制与决策, 2013, 28 (5): 683-687. DOI:10.13195/j.cd.2013.05.46.wangj.025. WANG J, FENG J, HAN Z Y. Locally preserving PCA method based on manifold learning and its application in fault detection[J]. Control and Decision, 2013, 28 (5): 683-687. DOI: 10.13195/j.cd.2013.05.46. wangj.025.
[2]	王晶, 刘莉, 曹柳林, 等. 基于核Fisher包络分析的间歇过程故障诊断[J]. 化工学报, 2014, 65 (4): 1317-1326. DOI: 10.3969/j.issn.0438-1157.2014.04.023. WANG J, LIU L, CAO L L, et al. Fault diagnosis based on kernel Fisher envelope surface for batch processes[J]. CIESC Journal, 2014, 65 (4): 1317-1326. DOI: 10.3969/j.issn.0438-1157.2014.04.023.
[3]	JIANG Q C, YAN X F. Nonlinear plant-wide process monitoring using MI-spectral clustering and Bayesian inference-based multiblock KPCA[J]. Journal of Process Control, 2015, 32 (1): 38-50. DOI: 10.1016/j.jprocont.2015.04.014
[4]	张妮, 田学民, 蔡连芳. 基于RISOMAP的非线性过程故障检测方法[J]. 化工学报, 2013, 64 (6): 2125-2130. DOI: 10.3969/j.issn.0438-1157.2013.06.031. ZHANG N, TIAN X M, CAI L F. Non-linear process fault detection method based on RISOMAP[J]. CIESC Journal, 2013, 64 (6): 2125-2130. DOI: 10.3969/j.issn.0438-1157.2013.06.031.
[5]	YANG X F, GOH A, QIU A Q. Locally linear diffeomorphic metric embedding (LLDME) for surface-based anatomical shape modeling[J]. Neuroimage, 2011, 56 (1): 149-161. 10.1016/j.neuroimage.2011.01.069.
[6]	MIAO A M, GE Z Q, SONG Z H, et al. Nonlocal structure constrained neighborhood preserving embedding model and Its application for fault detection[J]. Chemometrics and Intelligent Laboratory Systems, 2015, 142: 184-196. DOI:10.1016/j.chemolab. 2015.01.010.
[7]	JING C, YANG L. Locally linear embedding: a survey[J]. Artificial Intelligence Review, 2011, 36 (1): 29-48. DOI: 10.1007/s10462-010-9200-z.
[8]	SHAN R F, CAI W S, SHAO X G. Variable selection based on locally linear embedding mapping for near-infrared spectral analysis[J]. Chemometrics and Intelligent Laboratory Systems, 2014, 131: 31-36. DOI:10.1016/j.chemolab.2013.12.002.
[9]	SLUBAN B, LAVRA? N. Relating ensemble diversity and performance: a study in class noise detection[J]. Neurocomputing, 2015, 160: 120-131. DOI: 10.1016/j.neucom.2014.10.086.
[10]	XIE X X, HU J Z, XU F Y, et al. A fault diagnosis method using multi-manifold learning based on locally linear embedding[J]. Chinese Journal of Mechanical Engineering, 2013, 49 (11): 79-83. DOI: 10.3901/JME.2013.11.079.
[11]	ANDRÉS Á-M, JULIANA V-A, GENARO D-S, et al. Global and local choice of the number of nearest neighbors in locally linear embedding[J]. Pattern Recognition Letters, 2011, 32 (16): 2171-2177. DOI: 10.1016/j.patrec.2011.05.011.
[12]	WANG J. Real local-linearity preserving embedding[J]. Neurocomputing, 2014, 136 (20): 7-13. DOI: 10.1016/j.neucom.2014. 01.040.
[13]	HETTIARACHCHI R, PETERS J F. Multi-manifold LLE learning in pattern recognition[J]. Pattern Recognition, 2015, 48 (9): 2947-2960. DOI: 10.1016/j.patcog.2015.04.003.
[14]	马玉鑫, 王梦灵, 侍洪波. 基于局部线性嵌入算法的化工过程故障检测[J]. 化工学报, 2012, 63 (7): 2121-2127. DOI: 10.3969/j.issn. 0438-1157.2012.07.018. MA Y X, WANG M L, SHI H B. Fault detection for chemical process based on locally linear embedding[J]. CIESC Journal, 2012, 63 (7): 2121-2127. DOI: 10.3969/j.issn.0438-1157. 2012.07.018.
[15]	LI B W, ZHANG Y. Supervised locally linear embedding projection (SLLEP) for machinery fault diagnosis[J]. Mechanical Systems and Signal Processing, 2011, 25 (8): 3125-3134. DOI: 10.1016/j.ymssp. 2011.05.001.
[16]	周世兵, 徐振源, 唐旭清. 新的k均值算法最佳聚类数确定方法[J]. 计算机工程与应用, 2010, 46 (16): 27-31. DOI: 10.3778/j.issn.1002-8331.2010.16.008. ZHOU S B, XU Z Y, TANG X Q. New method for determining optimal number of clusters in k-means clustering algorithm[J]. Computer Engineering and Applications, 2010, 46 (16): 27-31. DOI: 10.3778/j.issn.1002-8331.2010.16.008.
[17]	ZHOU C Y, CHEN Y Q. Improving nearest neighbor classification with cam weighted distance[J]. Pattern Recognition, 2006, 39 (4): 635-645. DOI: 10.1016/j.patcog.2005.09.004.
[18]	SAKTHIVEL N R, NAIR B B, ELANGOVAN M, et al. Full length article: comparison of dimensionality reduction techniques for the fault diagnosis of mono block centrifugal pump using vibration signals[J]. Engineering Science and Technology, 2014, 17 (1): 30-38. DOI: 10.1016/j.jestch.2014.02.005.
[19]	CHEN H H, TI?O P, YAO X. Cognitive fault diagnosis in Tennessee Eastman process using learning in the model space[J]. Computers and Chemical Engineering, 2014, 67: 33-42. DOI: 10.1016/j.compchemeng. 2014.03. 015.
[20]	宋冰, 马玉鑫, 方永锋, 等. 基于LSNPE 算法的化工过程故障检测[J]. 化工学报, 2014, 65 (2): 620-627. DOI: 10.3969/j.issn.0438-1157.2014.02.036. SONG B, MA Y X, FANG Y F, et al. Fault detection for chemical process based on LSNPE method[J]. CIESC Journal, 2014, 65 (2): 620-627. DOI: 10.3969/j.issn.0438-1157.2014.02.036.

基于聚类选择k近邻的LLE算法及故障检测

Neighborhood selection of LLE based on cluster for fault detection

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献 20

相关文章 15

编辑推荐

Metrics

本文评价

[1]	张逸豪, 王振雷. 基于最大信息系数的分组支持向量数据描述故障检测[J]. 化工学报, 2023, 74(9): 3865-3878.
[2]	邵远哲, 赵忠盖, 刘飞. 基于共同趋势模型的非平稳过程质量相关故障检测方法[J]. 化工学报, 2023, 74(6): 2522-2537.
[3]	宋冰, 郑城风, 侍洪波, 陶阳, 谭帅. 基于VAE-OCCA的质量相关故障检测方法研究[J]. 化工学报, 2023, 74(4): 1630-1638.
[4]	杨明辉, 刘晓月, 邓晓刚, 廖明燕, 侯春望. 基于加权概率CVDA的动态化工系统微小故障检测[J]. 化工学报, 2022, 73(9): 3963-3972.
[5]	雍加望, 赵倩倩, 冯能莲. 基于非线性动态模型的质子交换膜燃料电池故障诊断[J]. 化工学报, 2022, 73(9): 3983-3993.
[6]	郭金玉, 王哲, 李元. 基于核熵独立成分分析的故障检测方法[J]. 化工学报, 2022, 73(8): 3647-3658.
[7]	王琨, 侍洪波, 谭帅, 宋冰, 陶阳. 局部时差约束邻域保持嵌入算法在故障检测中的应用[J]. 化工学报, 2022, 73(7): 3109-3119.
[8]	郭金玉, 李文涛, 李元. 在线压缩KECA的自适应算法在故障检测中的应用[J]. 化工学报, 2021, 72(8): 4227-4238.
[9]	李元, 杨东昇, 赵丽颖, 张成. 层次变分高斯混合模型与主多项式分析的故障检测策略[J]. 化工学报, 2021, 72(3): 1616-1626.
[10]	王晓慧, 王延江, 邓晓刚, 张政. 基于加权深度支持向量数据描述的工业过程故障检测[J]. 化工学报, 2021, 72(11): 5707-5716.
[11]	邓明月, 刘建昌, 许鹏, 谭树彬, 商亮亮. 基于KECA的非线性工业过程故障检测与诊断新方法[J]. 化工学报, 2020, 71(5): 2151-2163.
[12]	韩宇, 李俊芳, 高强, 田宇, 禹国刚. 基于故障判别增强KECA算法的故障检测[J]. 化工学报, 2020, 71(3): 1254-1263.
[13]	徐静,王振雷,王昕. 基于非线性动态全局局部保留投影算法的化工过程故障检测[J]. 化工学报, 2020, 71(12): 5655-5663.
[14]	孙中建,杨博,齐楚,李宏光. 面向工业混杂系统故障检测的扩展数据逻辑分析方法[J]. 化工学报, 2020, 71(11): 5237-5245.
[15]	于蕾, 邓晓刚, 曹玉苹, 路凯琪. 基于变量分组DTW-MCVA的不等长间歇过程故障检测方法[J]. 化工学报, 2019, 70(9): 3441-3448.