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.

Comparison of Semisupervised Deep Neural Networks for Anomaly Detection in Industrial Processes Anomaly detection methods are used for fast and reliable detection of abnormal events in industrial processes. The early detection of anomalies can avoid critical process breakdowns and hence can increase the overall productivity of the system. The availability of labelled datasets for all the possible faulty scenarios is generally not possible, as most of the industrial systems operate in a nonfaulty condition. Deep learning architectures that can be trained in an unsupervised setting such as deep autoencoders, denoising autoencoder and variational autoencoder provide an appropriate solution to this problem of unlabelled data for industrial anomaly detection. We investigate and compare the applicability of these architectures on the benchmark Tennessee Eastman fault detection study. The deep architectures are trained to model only the normal operating condition with its threshold set by kernel density estimation. A detailed comparison from the experimental results shows superior anomaly detection capabilities of the variational autoencoder as compared to the other methods.