Extracting Nonstationary Features for Process Data Analytics and Application in Fouling Detection Abstract   With advances in database and machine learning, datadriven methods are becoming more feasible for process monitoring. Among these methods, latent variable modeling approaches, which extract representative latent features to capture the essential behavior of process data, are more favorable. In this study, understanding the cumulating behavior in chemical processes (the nonstationary trend) is considered as the desired latent feature. Based on an emerging data analysis technique, we propose a novel feature extraction method. Instead of focusing on the stationary features, the proposed method focuses on the nonstationary trend using cointegration analysis. Its learning procedure is illustrated through maximum likelihood estimation, and its effectiveness is investigated through numerical simulations. Furthermore, by using the proposed approach, an application of monitoring fouling buildup is discussed, where the principal component analysis is appended for improving robustness. Practicality of this application is demonstrated through applications on three operating steam generators.

A new unsupervised data mining method based on the stacked autoencoder for chemical process fault diagnosis Abstract   Process monitoring plays an important role in chemical process safety management, and fault diagnosis is a vital step of process monitoring. Among fault diagnosis researches, supervised ones are inappropriate for industrial applications due to the lack of labeled historical data in real situations. Thereby, unsupervised methods which are capable of dealing with unlabeled data should be developed for fault diagnosis. In this work, a new unsupervised data mining method based on deep learning is proposed for isolating different conditions of chemical process, including normal operations and faults, and thus labeled database can be created efficiently for constructing fault diagnosis model. The proposed method mainly consists of three steps: feature extraction by the convolutional stacked autoencoder (SAE), feature visualization by the tdistributed stochastic neighbor embedding (tSNE) algorithm, and clustering. The benchmark Tennessee Eastman process (TEP) and an industrial hydrocracking instance are utilized to illustrate the effectiveness of the proposed data mining method.