化工学报 ›› 2023, Vol. 74 ›› Issue (6): 2522-2537.DOI: 10.11949/0438-1157.20230066
收稿日期:
2023-01-31
修回日期:
2023-05-01
出版日期:
2023-06-05
发布日期:
2023-07-27
通讯作者:
赵忠盖
作者简介:
邵远哲(1997—),男,硕士研究生,1983063392@qq.com
基金资助:
Yuanzhe SHAO(), Zhonggai ZHAO(), Fei LIU
Received:
2023-01-31
Revised:
2023-05-01
Online:
2023-06-05
Published:
2023-07-27
Contact:
Zhonggai ZHAO
摘要:
现有质量相关监控方法基于数据平稳的假设,而实际生产中存在大量的非平稳过程。针对上述问题,提出了一种基于共同趋势模型的非平稳过程质量相关故障检测方法。该方法首先识别出系统中的非平稳过程变量和质量变量,再利用Gonzalo-Granger分解求解共同趋势模型,从而分离非平稳数据中的平稳部分和非平稳部分,然后,整合平稳数据,以及非平稳数据的平稳子空间整合,应用慢特征分析(slow feature analysis, SFA)和典型相关分析(canonical correlation analysis, CCA)建立质量相关的监控模型,实现对非平稳质量变量的有效监控。最后通过对比实验,证明所提出方法可以有效发现非平稳过程质量相关故障。
中图分类号:
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.
Yuanzhe SHAO, Zhonggai ZHAO, Fei LIU. Quality-related non-stationary process fault detection method by common trends model[J]. CIESC Journal, 2023, 74(6): 2522-2537.
阶段 | 性能 衡量指标 | CCA | 残差序列方法 | QRCTSFA | ||
---|---|---|---|---|---|---|
无故障阶段 | FAR/% | 49.25 | 28.40 | 1.15 | 0.65 | 1.30 |
故障一(质量相关) | MDR/% | 39.10 | 0 | 12.50 | 0.20 | 94.80 |
故障二(质量相关) | MDR/% | 71.00 | 51.00 | 58.30 | 65.40 | 55.60 |
故障三(质量无关) | FAR/% | 59.90 | 81.00 | 1.80 | 0 | 1.40 |
表1 数值仿真三种方法质量相关监控性能
Table 1 Quality-related monitoring performance of three methods in numerical simulation
阶段 | 性能 衡量指标 | CCA | 残差序列方法 | QRCTSFA | ||
---|---|---|---|---|---|---|
无故障阶段 | FAR/% | 49.25 | 28.40 | 1.15 | 0.65 | 1.30 |
故障一(质量相关) | MDR/% | 39.10 | 0 | 12.50 | 0.20 | 94.80 |
故障二(质量相关) | MDR/% | 71.00 | 51.00 | 58.30 | 65.40 | 55.60 |
故障三(质量无关) | FAR/% | 59.90 | 81.00 | 1.80 | 0 | 1.40 |
阶段 | 性能衡量指标 | CCA | QRCTSFA | |
---|---|---|---|---|
正常集T2 | FAR/% | 100.00 | 0.03 | 0.81 |
案例一(故障集2.1) | FAR/% | 100.00 | 0.21 | 1.20 |
表2 三相流实验CCA、QRCTSFA质量相关指标对T2和案例一误报率
Table 2 False alarm rates of CCA and QRCTSFA for T2 and Case 1 in three-phase flow process
阶段 | 性能衡量指标 | CCA | QRCTSFA | |
---|---|---|---|---|
正常集T2 | FAR/% | 100.00 | 0.03 | 0.81 |
案例一(故障集2.1) | FAR/% | 100.00 | 0.21 | 1.20 |
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