A new unsupervised data mining method based on the stacked autoencoder for chemical process fault diagnosis

• Published in: Computers & chemical engineering Vol. 135; p. 106755
• Main Authors: Zheng, Shaodong, Zhao, Jinsong
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 06.04.2020
• Subjects: Clustering; Data mining; Fault diagnosis; The SAE; The TEP; Unsupervised; Fault diagnosis; The TEP; Unsupervised; The SAE; Data mining; Clustering
• ISSN: 0098-1354; 1873-4375
• DOI: 10.1016/j.compchemeng.2020.106755

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 t-distributed stochastic neighbor embedding (t-SNE) 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.