摘要
概率偏最小二乘(PPLS)模型建立的条件是主元和误差都服从高斯分布,但是高斯分布的期望和方差容易受到离群点的影响,导致模型的鲁棒性较差。针对PPLS模型的不足,提出一种鲁棒概率偏最小二乘(RPPLS)方法,用拖尾更宽的T分布代替高斯分布,通过调整自由度参数,使模型对含离群点数据的拟合效果更好。更进一步,将RPPLS引入过程监控中,提出GT2和GSPE两个监控指标,分别监控过程的受控状态以及模型关系的变化。PPLS和RPPLS在TE过程监控的应用结果表明RPPLS不仅能更准确检测故障的产生,而且能更有效降低故障的漏报率。
A probability model can be developed by probabilistic partial least squares（PPLS） under the conditions that both principal components and errors satisfy Gaussian distribution.However,the expectation and variance of the Gaussian distribution is susceptible to outliers.As a result,the model is not robust for the real industrial process.This paper improves the robustness of the PPLS model based on the assumption that the raw data satisfy T distribution rather than Gaussian distribution.By adjusting the freedom degree of T distribution,the proposed robust probabilistic partial least squares（RPPLS） model can overcome the shortcomings of PPLS model.Furthermore,on the basis of RPPLS model,two monitoring indicators GT2 and GSPE are proposed to monitor the process state and the model changes,respectively.Comparing the monitoring performance in the TE process based on PPLS and RPPLS shows that RPPLS is more effective than PPLS in terms of the fault accuracy and the missing alarm rate.
出处
《化工学报》
EI
CAS
CSCD
北大核心
2016年第7期2907-2915,共9页
CIESC Journal
基金
国家自然科学基金项目(61573169)
江苏省六大人才高峰项目(2014-ZBZZ-010)
关键词
鲁棒概率偏最小二乘算法
T分布
参数估值
监控指标
模型
robust probabilistic partial least squares algorithm
T distribution
parameter estimation
monitoring indices
model
