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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2021, Vol. 55 Issue (10): 1856-1866    DOI: 10.3785/j.issn.1008-973X.2021.10.007
    
On-line fault monitoring technology for industrial stationary/nonstationary complex system
Xiang-yu KONG(),Xiao-bing WANG,Hong-zeng LI,Jia-yu LUO
Department of Missile Engineering, Rocket Force University of Engineering, Xi’an 710000, China
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Abstract  

A KPI related fault detection method based on double-layer improved projection to latent structures (DL-IPLS) was proposed in view of the low detection accuracy of key performance indicators (KPI) related fault detection methods in complex systems. The underlying model was established by using cointegration analysis and principal component analysis in order to extract the features of non-stationary and stationary variables. Then the extracted information was fused to establish the upper model of improved projection to latent structures. Statistics were designed in two orthogonal subspaces to realize on-line monitoring of KPI related faults. The simulation results of Tennessee-Eastman process and penicillin fermentation process show that the proposed method can effectively improve the detection rate of KPI related faults for industrial stationary and non-stationary complex industrial systems, and reduce the false alarm rate of KPI independent fault.

Key words： data driven      cointegration analysis      improved projection to latent structures      key performance indicator      process monitoring      fault detection     
Received: 23 January 2021      Published: 27 October 2021
CLC:  TP 277  
Fund:  国家自然科学基金资助项目（61673387，61833016）；陕西省自然科学基金资助项目（2020JM-356）
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Xiang-yu KONG
Xiao-bing WANG
Hong-zeng LI
Jia-yu LUO
Cite this article:

Xiang-yu KONG,Xiao-bing WANG,Hong-zeng LI,Jia-yu LUO. On-line fault monitoring technology for industrial stationary/nonstationary complex system. Journal of ZheJiang University (Engineering Science), 2021, 55(10): 1856-1866.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2021.10.007     OR     https://www.zjujournals.com/eng/Y2021/V55/I10/1856


面向工业平稳/非平稳复杂系统的在线故障监测技术

针对复杂系统中关键性能指标（KPI）相关故障检测方法检测精度低的问题，提出基于双层改进潜结构投影（DL-IPLS）的KPI相关故障检测方法. 利用协整分析和主元分析建立底层模型，对非平稳和平稳变量进行特征提取. 将提取的信息进行融合，建立改进潜结构投影的上层模型，根据融合信息对KPI的贡献进行空间分解. 在2个正交子空间中设计统计量，实现KPI相关故障的在线监测. 田纳西-伊斯曼过程和青霉素发酵过程的仿真结果表明，在面向工业平稳和非平稳复杂工业系统检测时，所提方法有效提高了KPI相关故障的检测率，降低了KPI无关故障的误报率.


关键词： 数据驱动,  协整分析,  改进潜结构投影,  关键性能指标,  过程监控,  故障检测 
Fig.1 Fault monitoring flow chart based on DL-IPLS
监测算法 ${J_{{\rm{th,}}{T}_{\hat x}^2}}$ ${J_{{\rm{th,}}{T}_{\tilde x}^2}}$
MPLS 6.70 58.58
OSC-MPLS 7.10 52.75
IPLS 11.53 55.46
DL-IPLS 17.24 20.71
Tab.1 Control limits of four algorithms in TE process
故障编号 KPI相关故障描述 FDR /%
MPLS OSC-MPLS IPLS DL-IPLS
IDV（1） A/C进料流量比发生变化，成分B恒定 89.87 89.00 99.50 99.65
IDV（2） 组分B质量浓度发生变化，A/C供料比恒定 88.12 96.75 94.37 98.50
IDV（5） 冷凝器冷却水的入口温度 100 100 25.25 24.38
IDV（6） A供料损失（管道1） 99.12 98.75 99.12 99.13
IDV（7） C存在压力损失 41.12 49.37 73.00 99.63
IDV（8） 物料A，B，C供料质量浓度（管道4） 67.12 77.37 94.75 90.88
IDV（10） 物料C供料温度发生变化 46.00 54.37 46.12 77.87
IDV（12） 压缩机冷凝水入口温度变化 84.12 86.37 93.37 91.13
IDV（13） 反应器中的反应程度 90.37 89.87 90.12 92.38
IDV（17） 未知 54.87 65.37 56.50 69.25
IDV（18） 未知 90.00 89.75 88.50 89.13
IDV（20） 未知 49.25 68.25 39.25 75.88
IDV（21） 阀固定在稳态位置 70.25 75.87 56.62 34.50
Tab.2 FDR of KPI related faults in TE process
Fig.2 Detection results of KPI related fault IDV (5) by DL-IPLS
Fig.3 Detection results of KPI related fault IDV (7) by DL-IPLS
Fig.4 Detection results of KPI related fault IDV (10) by OSC-MPLS
Fig.5 Detection results of KPI related fault IDV (10) by IPLS
Fig.6 Detection results of KPI related fault IDV (10) by DL-IPLS
故障编号
KPI无关故障描述 FAR /%
MPLS OSC-MPLS IPLS DL-IPLS
IDV（3） 物料D供料温度发生变化 13.62 13.50 7.87 1.37
IDV（4） 反应器冷却水入口温度变化 11.00 9.37 22.00 1.37
IDV（9） D供料温度发生变化 7.50 5.00 9.12 1.12
IDV（11） 反应冷却水入口温度变化 9.62 8.50 21.37 3.37
IDV（15） 压缩机冷凝水阀门 10.51 10.75 13.25 0.62
IDV（16） 未知 45.86 35.00 28.38 66.00
IDV（19） 未知 7.00 10.88 3.38 1.25
Tab.3 FAR of KPI-unrelated faults in TE process
Fig.7 Detection results of KPI unrelated fault IDV (4) by OSC-MPLS
Fig.8 Detection results of KPI unrelated fault IDV (4) by IPLS
Fig.9 Detection results of KPI unrelated fault IDV (4) by DL-IPLS
标号 变量 标号 变量
1 采样时间 10 反应器体积
2 通风速率 11 排气二氧化碳浓度
3 搅拌速率 12 PH值
4 底物流加速率 13 温度
5 补料温度 14 产生热
6 底物物质的量 15 酸流加速率
7 溶解氧物质的量 16 碱流加速率
8 菌体物质的量 17 冷水流加速率
9 产物物质的量 18 热水流加速率
Tab.4 Pensim2.0 simulation data variable
Fig.10 Detection results of fault 1 in penicillin fermentation by DL-IPLS
Fig.11 Detection results of fault 2 in penicillin fermentation by DL-IPLS
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