Case Study: A SemiSupervised Methodology for Anomaly Detection and Diagnosis In this paper, a semisupervised methodology for anomaly detection and diagnosis is proposed. The approach combines techniques of nonparametric statistics, quality control, and deep learning to provide a tool that allows an adequate and online detection of faults in a production system and a diagnosis of the factors associated with the failure. We propose a semisupervised neural network for detection and a particular control chart called Open Up for the diagnosis. This neural network is composed of the adjustment of an autoencoder followed by a Long ShortTerm Memory model (LSTM). Open Up is used in the last stage to identify the variables associated with the anomaly. This proposal achieves a high correct classification rate using real data of a monitoring system in paper manufacturing and simulated data from the Tennessee Eastman Process.

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.