Industrial Process Monitoring and Fault Diagnosis Based on Hybrid Discriminant Analysis
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摘要: 工业过程数据具有规模性大、复杂性高、变量多、关联性强等特点.如何从数据出发准确并快速地发现故障并处理, 保证过程高效运行意义重大.本文针对复杂的工业过程, 提出了一种多方法结合的混合型过程监控与故障诊断方法, 完成数据分类, 构建故障模型库, 故障在线诊断及可视化相关处理.首先通过常规主成分分析(Principal component analysis, PCA)方法对历史数据进行初筛, 区分出正常和故障信息, 然后利用聚类方法对故障数据集进行分类, 接着利用局部线性指数判别分析方法(Local linear exponential discriminant analysis, LLEDA)建立故障模型库进而进行故障诊断.本文将基于监督学习的LLEDA方法拓展到无监督学习, 便于复杂工业大量无标签数据的处理.最后利用典型的田纳西伊士曼(Tennessee Eastman, TE)过程对所提出的方法进行有效性验证.
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关键词:
- 复杂工业过程 /
- 混合型故障诊断 /
- 局部线性指数判别分析 /
- 可视化
Abstract: Industrial process data has the characteristics of large scale, high complexity, multivariable and strong correlation. There is great signiflcance on how to flnd out and deal with the fault from data accurately and quickly, which can help ensure the e–cient operation of the process. The paper proposed a hybrid multi-method process monitoring and fault diagnosis framework for the complex industrial process. The framework include the data classiflcation, model library establishment, timely diagnosis. Firstly, the historical data is simply screened by principal component analysis (PCA) methods to distinguish normal and fault information. Then the clustering method is used to classify the fault data set, and the fault model libraries are established by local linear exponential discriminant analysis (LLEDA) method. Finally, the fault diagnosis is carried out. The LLEDA method based on supervised learning is extended to unsupervised learning, which facilitates the processing of a large number of unlabeled data in complex industries. Finally, a typical Tennessee Eastman process is used to verify the efiectiveness of the proposed method.-
Key words:
- Industrial process /
- hybrid fault diagnosis /
- local linear exponential discriminant analysis (LLEDA) /
- visualization
1) 本文责任编委 穆朝絮 -
表 1 TE故障汇总
Table 1 TE fault summary
编号 描述 类型 IDV (0) 正常操作 - IDV (1) A/C进料比率, B成分不变 阶跃 IDV (2) B成分, A/C进料比不变 阶跃 IDV (3) D的进口温度 阶跃 IDV (4) 反应器冷却水的入口温度 阶跃 IDV (5) 冷凝器冷却水的入口温度 阶跃 IDV (6) A进料损失 阶跃 IDV (7) C存在压力损失-可用性降低 阶跃 IDV (8) A、B、C进料成分 随机变量 IDV (9) D的进料温度 随机变量 IDV (10) C的进料温度 随机变量 IDV (11) 反应器冷却水的入口温度 随机变量 IDV (12) 冷凝器冷却水的入口温度 随机变量 IDV (13) 反应动态 慢漂移 IDV (14) 反应器冷却水阀门 粘住 IDV (15) 冷凝器冷却水阀门 粘住 IDV (16) 未知 未知 IDV (17) 未知 未知 IDV (18) 未知 未知 IDV (19) 未知 未知 IDV (20) 未知 未知 IDV (21) 阀固定在稳态位置 未知的恒定位置 表 2 基于PCA方法的故障识别率
Table 2 Fault recognition rate based on PCA method
故障识别率 故障1 故障2 故障3 故障4 故障5 故障6 故障7 故障8 故障9 故障10 故障11 故障12 故障13 故障14 故障15 故障16 故障17 故障18 故障19 故障20 故障21 T2 0.5212 0.995 0.9825 0.0225 0.41 0.2625 0.99 1 0.975 0.0362 0.4163 0.9875 0.9513 0.9988 0.0488 0.2325 0.8013 0.8912 0.0675 0.3738 0.3775 SPE 0.8163 0.9988 0.9925 0.2675 1 0.5025 1 1 0.9825 0.235 0.7638 0.99 0.9625 1 0.2625 0.6937 0.975 0.9375 0.5913 0.735 0.6687 表 3 不同方法下故障4, 8和13的识别率
Table 3 Recognition rate of fault 4, 8 and 13 under different methods
特征向量数 识别率 FDA LLE + EDA LLEDA 2 Fault 8 0.525 0.675 0.85 Fault 4 1 1 1 Fault 13 0.2275 0.37 0.265 3 Fault 8 0.525 0.67 0.955 Fault 4 1 1 1 Fault 13 0.215 0.375 0.125 4 Fault 8 0.5475 0.66 0.945 Fault 4 1 1 1 Fault 13 0.225 0.365 0.4 5 Fault 8 0.585 0.655 0.93 Fault 4 1 1 1 Fault 13 0.585 0.655 0.93 6 Fault 8 0.5925 0.68 0.9025 Fault 4 1 1 1 Fault 13 0.2525 0.345 0.5725 7 Fault 8 0.605 0.6475 0.8875 Fault 4 1 1 1 Fault 13 0.265 0.3925 0.6025 8 Fault 8 0.615 0.6775 0.8775 Fault 4 1 1 1 Fault 13 0.2475 0.375 0.62 9 Fault 8 0.6525 0.675 0.8925 Fault 4 1 1 1 Fault 13 0.2425 0.255 0.615 10 Fault 8 0.6625 0.7175 0.9125 Fault 4 1 1 1 Fault 13 0.26 0.2325 0.7325 -
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