山东大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (5): 44-50.doi: 10.6040/j.issn.1672-3961.0.2017.171
陈志文1, 彭涛1*, 阳春华1, 何章鸣2,杨超1, 杨笑悦1
CHEN Zhiwen1, PENG Tao1*, YANG Chunhua 1, HE Zhangming2, YANG Chao1, YANG Xiaoyue1
摘要: 为提高基于典型相关分析的故障检测方法使用效率,对原有的残差产生方式进行改进。通过分析残差信号统计特性,重新选取残差产生方式,使得改进的残差生成方式不依赖于主元个数的选取,从而避免因主元个数选取所带来的故障检测性能影响。通过Tennessee Eastman benchmark process仿真实例,对改进方法的可行性和有效性进行验证。选取4个典型故障的运行数据,分别用所提方法进行故障检测,改进的典型相关分析方法能够有效的检测故障的发生。另外,通过对两个统计量的故障检测率的对比可以看出,两个统计量对于发生在不同子空间的故障敏感度各异,对于不同故障的检测能力不同。
中图分类号:
[1] 周东华,叶银忠. 现代故障诊断与容错控制[M].北京:清华大学出版社,2000. [2] GERTLER J. Fault detection and diagnosis in engineering systems[M]. New York: Marcel Dekker, 1998. [3] DING S X. Model-based fault diagnosis techniques-design schemes, algorithms and tools[M]. 2nd ed. London: Springer-Verlag, 2013, [4] GE Zhiqiang, SONG Zhihuan, GAO Furong. Review of recent research on data-based process monitoring[J]. Industrial Engineering Chemical Research, 2013, 52(10):3543-3562. [5] ZHANG Kai, HAO Haiyang, CHEN Zhiwen, et al. A comparison and evaluation of key performance indicator-based multivariate statistics process monitoring approaches[J]. Journal of Process Control, 2015, 33:112-126. [6] YIN Shen, DING S X, HAGHANI A. 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, 22:1567-1581. [7] QIN S J. Survey on data-driven industrial process monitoring and diagnosis[J]. Annual Reviews in Control, 2012, 36(2):220-234. [8] YIN Shen, LIU Lei, HOU Jian. A multivariate statistical combination forecasting method for product quality evaluation[J]. Information Science, 2016, 355-356:229-236. [9] YIN Shen, WANG Guang, GAO Huijun. Data-driven process monitoring based on modified orthogonal projections to latent structures[J]. IEEE Transactions on Control System Technology, 2016, 24(4):1480-1487. [10] MAJID N A, TAYLOR M P, CHEN J J, et al. Aluminium process fault detection by multiway principal component analysis[J]. Control Engineering Practice, 2011, 19(4):367-379. [11] THORNIHILL N F, HORCH A. Advances and new directions in plant-wide disturbance detection and diagnosis [J]. Control Engineering Practice, 2007, 15(10):1196-1206. [12] ZHOU Donghua, LI Gang, QIN S J. Total projection to latent structure for process monitoring[J]. AIChE J, 2010,56(1):168-178. [13] MACGREGOR J F, KOURTI T. Statistical process control of multivariate processes[J]. Control Engineering Practice, 1995, 3(3):403-414. [14] KANO M, HASEBE S, HASHIMOTO I, et al. A new multivariate statistical process monitoring method using principal component analysis [J]. Computer Chemometrics Engineering, 2001, 25(7-8):1103-1113. [15] ZHANG Yingwei, ZHOU Hong, QIN S J, et al. Decentralized fault diagnosis of large-scale processes using multiblock kernel partial least squares[J]. IEEE Transactions on Industrial Informatics, 2010, 6(1):3-10. [16] DING S X. Data-driven design of fault diagnosis and fault-tolerant control systems[M]. London: Springer-Verlag, 2014. [17] CHEN Zhiwen, ZHANG Kai, DING S X, et al. Improved canonical correlation analysis-based fault detection methods for industrial processes[J]. Journal of Process Control, 2016, 41:26-34. [18] 彭开香, 马亮, 张凯. 复杂工业过程质量相关的故障检测与诊断技术综述[J]. 自动化学报, 2017, 43(2): 1-17. PENG Kaixiang, MA Liang, ZHANG Kai. Review of quality-related fault detection and diagnosis techniques for complex industrial processes [J]. Acta Automatica Sinica, 2017, 43(3): 349-365. [19] CHEN Zhiwen, DING S X, ZHANG Kai, et al. Canonical correlation analysis-based fault detection methods with application to alumina evaporation process [J]. Control Engineering Practice, 2016, 46:51-58. [20] ANDERSON T W. An introduction to multivariate statistical analysis[M]. Second edition. New York: John Wiley and Sons, LTD, 1984. [21] DOWNS J, FOGEL E. A plant-wide industrial process control problem[J]. Computer Chemistry Engineering, 1993, 17(3):245-255. |
[1] | 李广丽,刘斌,朱涛,殷依,张红斌. 基于优选典型相关分量的跨媒体检索模型[J]. 山东大学学报(工学版), 2018, 48(5): 38-46. |
[2] | 刘洋. 乘性故障对开闭环系统故障诊断性能的影响[J]. 山东大学学报(工学版), 2017, 47(5): 38-43. |
[3] | 杨瑞. 基于稀疏表示的间歇故障检测方法及仿真[J]. 山东大学学报(工学版), 2017, 47(5): 51-56. |
[4] | 李洪阳,何潇. 基于SCKF方法的非线性随机动态系统故障诊断方法[J]. 山东大学学报(工学版), 2017, 47(5): 130-135. |
[5] | 张米露,王天真,汤天浩,辛斌. 一种模式关联主元分析的海流机故障检测方法[J]. 山东大学学报(工学版), 2017, 47(5): 123-129. |
[6] | 陈杰,钟麦英,张利刚. 基于L2范数最小估计的无人机飞控系统故障检测[J]. 山东大学学报(工学版), 2017, 47(5): 89-95. |
[7] | 赵煊,钟麦英,郭丁飞. 基于等价空间的无人机飞行控制系统故障检测[J]. 山东大学学报(工学版), 2017, 47(5): 150-156. |
[8] | 季涛,孙同景,徐丙垠,孙波 . 直流系统接地故障综合检测方法[J]. 山东大学学报(工学版), 2006, 36(1): 55-59 . |
|