A Deep Learningbased Approach to Anomaly Detection with 2Dimensional Data in Manufacturing In modern manufacturing scenarios, detecting anomalies in production systems is pivotal to keep highquality standards and reduce costs. Even in the Industry 4.0 context, realworld monitoring systems are often simple and based on the use of multiple univariate control charts. Datadriven technologies offer a whole range of tools to perform multivariate data analysis that allow to implement more effective monitoring procedures. However, when dealing with complex data, common datadriven methods cannot be directly used, and a feature extraction phase must be employed. Feature extraction is a particularly critical operation, especially in anomaly detection tasks, and it is generally associated with information loss and low scalability. In this paper we consider the task of Anomaly Detection with twodimensional, imagelike input data, by adopting a Deep Learningbased monitoring procedure, that makes use of convolutional autoencoders. The procedure is tested on real Optical Emission Spectroscopy data, typical of semiconductor manufacturing. The results show that the proposed approach outperforms classical feature extraction procedures.

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.