基于独立元分析的制浆造纸废水处理过程故障检测

Fault Detection of Papermaking Wastewater Treatment Process Based on Independent Component Analysis

中文关键词:  制浆造纸废水处理过程  故障检测  主成分分析  独立元分析

Key Words:papermaking wastewater treatment process  fault detection  principal component analysis  independent component analysis

中文摘要:

      为及时、准确地做出故障诊断，本课题采用独立元分析（ICA）和主成分分析（PCA）两种常用的多元统计分析方法对制浆造纸废水处理过程中的传感器故障进行检测并对诊断效果进行对比。结果表明，对于制浆造纸废水数据中偏移和漂移两种故障，ICA模型的故障检测率分别为24%与54%，PCA模型的故障检测率分别为14%和42%，ICA模型的两种故障检测率均高于PCA模型，但是两种模型均无法达到满意的检测效果；对于完全失效故障，ICA和PCA模型的故障检测率均达到100%。

Abstract:

      To monitor and control papermaking wastewater treatment process(WWTP) effectively, two common methods of multivariate statistical analysis named independent component analysis (ICA) and principal component analysis (PCA) were used to detect the sensor faults in a papermaking WWTP.The results showed that the detection rates of the bias and drifting faults using ICA were 24% and 54%, respectively.Meanwhile, the bias and drifting faults detection rates of PCA were 14% and 42%.The fault detection rates of ICA were higher than those of PCA, but neither of the two methods achieved satisfactory result of detecting the bias and drifting faults.Concerning the complete failure fault, both the fault detection rates of the two methods were 100%.