The aim of this paper is to frame Data Science, a fashion and emerging topic nowadays in
the context of business and industry. We open with a discussion about the origin of Data …

The present paper brings together openly available datasets and simulators for testing of
process monitoring and fault diagnosis techniques. Some general characteristics of these …

Intrusion detection is a challenging technology in the area of cyberspace security for
protecting a system from malicious attacks. A novel accurate and effective misuse intrusion …

The move towards advanced manufacturing and Industry 4.0 is fed by increased demand for
speeding up innovation, increasing flexibility, improving maintenance, and becoming more …

In recent years, there have been studies focusing on the use of different types of
autoencoders (AEs) for monitoring complex nonlinear data coming from industrial and …

Abstract The Tennessee Eastman Process (TEP) is a frequently used benchmark in
chemical engineering research. An extended simulator, published in 2015, enables a more …

Independent component analysis (ICA) is a commonly used non-Gaussian process fault
diagnosis method. A fault detection algorithm of kernel dynamic ICA (KDICA) has been …

The proliferation of automated data collection schemes and the advances in sensorics are
increasing the amount of data we are able to monitor in real-time. However, given the high …

This study compares four models for industrial condition monitoring including a principal
components analysis (PCA) approach and three deep learning models, one of which is a …

This paper presents a new integrated methodology for fault detection and diagnosis. The
methodology is built using the multivariate exponentially weighted moving average principal …