Unsupervised Online Anomaly Detection on Multivariate Sensing Time Series Data for Smart Manufacturing The emergence of IoT and AI has brought revolutionary change in various application domains. One of them is Industry 4.0, also called Smart Manufacturing, which aims to achieve highly flexible and automated production processes. In this paper, we study a use case of anomaly detection in smart manufacturing using the real data collected from the sensing devices of a factory production line. Our goal is to improve the anomaly detection accuracy at an earlier stage of production line, so that cost and time wasted by possible production failures can be reduced. To overcome the limited and irregular anomaly patterns found from our multivariate sensor dataset, we proposed an unsupervised realtime anomaly detection algorithm based on LSTMbased AutoEncoder. Our evaluations show that our approach achieved almost 90% accuracy for both precision and recall while other classification or regression based methods only reached 70%85%.

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.