基于动态MDONPE算法的间歇过程故障检测

兰州理工大学学报 ›› 2022, Vol. 48 ›› Issue (2): 90-96.

• 自动化技术与计算机技术 • 上一篇下一篇

基于动态MDONPE算法的间歇过程故障检测

赵小强^*1,2,3, 刘凯¹

1.兰州理工大学电气工程与信息工程学院, 甘肃兰州 730050;
2.兰州理工大学甘肃省工业过程先进控制重点实验室, 甘肃兰州 730050;
3.兰州理工大学国家级电气与控制工程实验教学中心, 甘肃兰州 730050

收稿日期:2020-08-27 出版日期:2022-04-28 发布日期:2022-05-07
通讯作者: 赵小强(1969-),男,陕西岐山,博士,教授,博导.Email:xqzhao@lut.edu.cn
基金资助:
国家自然科学基金(61763029),甘肃省科技计划资助项目(21YF5GA072, 21JR7RA206),国家重点研发计划项目(2020YFB1713600),甘肃省教育厅产业支撑计划项目(2021CYZC-02)

Fault detection of batch process based on dynamic MDONPE algorithm

ZHAO Xiao-qiang^1,2,3, LIU Kai¹

1. College of Electrical and Information Engineering, Lanzhou Univ. of Tech., Lanzhou 730050, China;
2. Gansu Key Laboratory of Advanced Control for Industrial Processes, Lanzhou Univ. of Tech., Lanzhou 730050, China;
3. National Experimental Teaching Center of Electrical and Control Engineering, Lanzhou Univ. of Tech., Lanzhou 730050, China

Received:2020-08-27 Online:2022-04-28 Published:2022-05-07

摘要/Abstract

摘要： 针对间歇过程数据存在的非线性和动态特性导致故障检测效果不佳的问题,提出一种基于滑动窗(sliding window,SW)的多向差分正交邻域保持嵌入(multiway differential orthogonal neighborhood preserving embedded,MDONPE)算法.首先对间歇过程数据进行预处理,找到样本的最近邻,将样本与最近邻进行差分运算;然后对NPE算法进行投影向量正交化得到具有正交约束的正交邻域保持嵌入算法,利用正交邻域保持嵌入算法进行降维和特征提取,进一步利用滑动窗策略,选择合适的窗口宽度,合并窗口内的采样数据,使得故障样本的特征更加明显;最后通过检测T² 和SPE统计量判断是否发生故障.利用青霉素发酵仿真过程数据并与MPCA、KNPE算法进行对比验证,结果显示SW-MDONPE算法在故障检测中对比其他算法有更好的检测效果.

关键词: 间歇过程, 故障检测, 正交邻域保持嵌入, 差分策略, 滑动窗

Abstract: To solve the problem of poor fault detection effect due to the nonlinear and dynamic characteristics of the data in the batch process, a multiway differential orthogonal neighborhood preserving embedding (MDONPE) algorithm based on the sliding window (SW) is proposed. Firstly, the data of the batch process is preprocessed to find the nearest neighbors of the samples, and the difference operations between the samples and the nearest neighbors is carried out. Then the orthogonal neighborhood preserving embedding algorithm with orthogonal constraints is obtained by orthogonalizing NPE algorithm, and the orthogonal neighborhood preserving embedding algorithm is used to reduce dimensions and extract features. The sliding window strategy is used to combine and achieve the error accumulation by selecting the sampling data within the window width, which can make the features of the fault samples more obvious. Finally, the fault is judged by detection, and the T² and SPE statistics are used to judge if faults have occurred. The data of the Penicillin fermentation simulation process are used and compared with MPCA and KNPE algorithms. The results show that the proposed algorithm has better detection effect than other algorithms in fault detection.

Key words: batch process, fault detection, orthogonal neighborhood preserving embedded, difference strategy, sliding window

中图分类号:

TP277

赵小强, 刘凯. 基于动态MDONPE算法的间歇过程故障检测[J]. 兰州理工大学学报, 2022, 48(2): 90-96.

ZHAO Xiao-qiang, LIU Kai. Fault detection of batch process based on dynamic MDONPE algorithm[J]. Journal of Lanzhou University of Technology, 2022, 48(2): 90-96.

参考文献

[1] JIANG Q C,YAN S F,YAN X F,et al.Data-driven two-dimensional deep correlated representation learning for nonlinear batch process monitoring [J].IEEE Transactions on Industrial Informatics,2020,16(4):2839-2848.
[2] 李春阳,夏丽莎,李军祥.KNPE算法在化工过程故障检测中的应用 [J].控制工程,2020,27(1):92-97.
[3] ZHANG H Y,TIAN X M,DENG X G,et al.Batch process fault detection and identification based on discriminant global preserving kernel slow feature analysis [J].ISA Transactions,2018,79:108-126.
[4] 文成林,吕菲亚.基于深度学习的故障诊断方法综述 [J].电子与信息学报,2020,42(1):234-248.
[5] 郭金玉,刘玉超,李元.一种基于改进局部熵PCA的工业过程故障检测方法 [J].高校化学工程学报,2019,33(4):922-932.
[6] 张成,高宪文,徐涛,等.基于独立元的k近邻故障检测策略 [J].控制理论与应用,2018,35(6):805-812.
[7] 赵帅,宋冰,侍洪波.基于加权互信息主元分析算法的质量相关故障检测 [J].化工学报,2018,69(3):962-973.
[8] 梁春燕,曹伟.基于邻域保持嵌入算法的语种识别 [J].陕西师范大学学报(自然科学版),2020,48(2):38-42.
[9] 王族,闫德勤,何阳,等.基于NPE改进算法的人脸识别 [J].软件导刊,2019,18(2):9-12.
[10] HUI Y Y,ZHAO X Q.Multi-phase batch process monitoring based on multiway weighted global neighborhood preserving embedding method [J].Journal of Process Control,2018,69:44-57.
[11] 杨健,宋冰,谭帅,等.时序约束NPE算法在化工过程故障检测中的应用 [J].化工学报,2016,67(12):5131-5139.
[12] JIANG Q C,YAN X F.Probabilistic weighted NPE-SVDD for chemical process monitoring [J].Control Engineering Practice,2014,28:74-89.
[13] 仝奇,胡双演,李钊,等.基于KPCA的BP神经网络齿轮泵故障诊断方法研究 [J].无线电工程,2015,45(9):72-76.
[14] 常鹏,乔俊飞,王普,等.基于MKECA的非高斯性和非线性共存的间歇过程监测 [J].化工学报,2018,69(3):1200-1206.
[15] 赵小强,王涛.基于SPA间歇过程故障诊断的MKNPE算法 [J].兰州理工大学学报,2016,42(3):82-87.
[16] 张成,郭青秀,冯立伟,等.基于局部近邻标准化和动态主元分析的故障检测策略 [J].计算机应用,2018,38(9):2730-2734.
[17] 张忠祥,程辉,叶贞成,等.用于动态化工过程故障检测的T-TELPP算法 [J].华东理工大学学报(自然科学版),2018,44(4):496-503.
[18] LUO L J,BAO S Y.Knowledge-data-integrated sparse modeling for batch process monitoring [J].Chemical Engineering Science,2018,189:221-232.
[19] 赵化良.基于传递熵的MPCA间歇过程监测方法 [J].计算机系统应用,2016,25(2):146-151.
[20] 郭金玉,齐蕾蕾,李元.基于DMOLPP的间歇过程在线故障检测 [J].仪器仪表学报,2015,36(1):134-142.
[21] 梁秀霞,郑向博,郑晓慧.基于邻域保持嵌入算法的间歇过程故障检测 [J].自动化与仪表,2015,30(10):1-4.
[22] 魏永合,马步芳,刘炜,等.基于VMD与正交局部保持投影的齿轮故障诊断 [J].组合机床与自动化加工技术,2020(3):3-6.
[23] 郭金玉,仲璐璐,李元.基于差分局部保持投影的非线性过程故障检测 [J].信息与控制,2018,47(2):200-205.
[24] 柯亮,熊伟丽,徐保国.基于滑动窗PCA的微小故障检测 [J].小型微型计算机系统,2016,37(6):1360-1364.
[25] 惠永永,赵小强.基于WICA-WGNPE的高斯-非高斯联合指标间歇过程监控 [J].仪器仪表学报,2018,39(1):190-199.
[26] BIROL G,UNDEY C,PARULEKAR S J,et al.A morphologically structured model for penicillin production [J].Biotechnology and Bioengineering,2002,77(5):538-552.

基于动态MDONPE算法的间歇过程故障检测

Fault detection of batch process based on dynamic MDONPE algorithm

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 2

编辑推荐

Metrics

本文评价

[1]	赵小强, 姚红娟. 基于DDNPE算法的间歇过程故障诊断[J]. 兰州理工大学学报, 2020, 46(4): 96-102.
[2]	赵小强, 张和慧. 基于改进NPE算法的间歇过程故障检测[J]. 兰州理工大学学报, 2020, 46(2): 86-91.