Variational restricted Boltzmann machines to automated anomaly detection

Abstract

Data-driven methods are implemented using particularly complex scenarios that reflect in-depth perennial knowledge and research. Hence, the available intelligent algorithms are completely dependent on the quality of the available data. This is not possible for real-time applications, due to the nature of the data and the computational cost that is required. This work introduces an Automatic Differentiation Variational Inference (ADVI) Restricted Boltzmann Machine (RBM) to perform real-time anomaly detection of industrial infrastructure. Using the ADVI methodology, local variables are automatically transformed into real coordinate space. This is an innovative algorithm that optimizes its parameters with mathematical methods by choosing an approach that is a function of the transformed variables. The ADVI RBM approach proposed herein identifies anomalies without the need for prior training and without the need to find a detailed solution, thus making the whole task computationally feasible. © 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.