半监督鲁棒概率偏最小二乘模型及其在多采样率过程监控中的应用

Semi-supervised Robust Probabilistic Partial Least Squares Model and Its Applications to Multi-rate Process Monitoring

针对实际工业过程中多采样率问题,引入半监督方法,提出一种半监督鲁棒概率偏最小二乘法,将采样率不一致的完整数据分成少数标记样本和大量未标记样本,然后分别用这两种样本数一致的数据建立鲁棒概率偏最小二乘(PPLS)模型,通过充分挖掘大量未标记数据提供的有用信息来提高模型的准确性.更进一步,将半监督鲁棒PPLS引入过程监控中,提出GT2、SPEx和SPEy三个监控指标,分别监控过程的受控状态以及模型关系的变化.通过对半监督鲁棒PPLS和降采样鲁棒PPLS在TE过程监控应用中比较,结果表明半监督鲁棒PPLS比降采样鲁棒PPLS效果更好.

We present a semi-supervised robust probabilistic partial least squares( semi-supervised RPPLS) method,which can handle data with unequal sample sizes of input variables and output variables. The model should be developed based on complete data samples. However,the dataset is divided into two parts. The first part that contains samples of both the process variables and corresponding quality variables is denoted as the labeled dataset. The other part that consists of the process variable samples only is called the unlabeled dataset. We employ the unlabeled dataset together with the labeled dataset to develop a valid statistical model. Furthermore,on the basis of the semi-supervised RPPLS model,three monitoring indices,namely,GT2,SPEx,and SPEy,are proposed to evaluate the process state and the model changes. A comparison indicates that the proposed method is more effective than the downsampling RPPLS method in the monitoring of the Tennessee Eastman process.