Semi-supervised process monitoring based on self-training PCA model

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Data-centric engineering has recently become as a hot spot in both areas of artificial intelligence and data science, which shifts the focus of engineering application from model to data itself. The model for data-driven process monitoring is usually developed upon a large number of normal data samples, which have been assumed to be well evaluated in advance. However, the actual quality of those training data samples has a great impact to the monitoring performance. If those samples with low quality are included for modeling, the monitoring performance could be severely deteriorated. In practice, due to expensive and time-consuming data quality evaluation procedures, we may only have a quite limited number of evaluated data samples, and hold a large number of unevaluated data samples. This paper aims to develop a semi-supervised monitoring model, which can simultaneously incorporate the evaluated dataset and the original unevaluated dataset. As a result, the new semi-supervised monitoring scheme can save a lot of human efforts, and could be particularly useful in those processes which can only provide a small portion of evaluated data samples in time. The feasibility and efficiency of the new proposed monitoring scheme are examined through case studies of a numerical example and the TE benchmark process.