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Variational restricted Boltzmann machines to automated anomaly detection

Demertzis, Konstantinos; Iliadis, Lazaros; Pimenidis, Elias; Kikiras, Panagiotis

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Konstantinos Demertzis

Lazaros Iliadis

Panagiotis Kikiras


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.

Journal Article Type Article
Acceptance Date Feb 5, 2022
Online Publication Date Mar 1, 2022
Publication Date 2022-09
Deposit Date Mar 16, 2022
Publicly Available Date Mar 2, 2023
Journal Neural Computing and Applications
Print ISSN 0941-0643
Electronic ISSN 1433-3058
Publisher Springer (part of Springer Nature)
Peer Reviewed Peer Reviewed
Volume 34
Pages 15207–15220
Keywords Automatic differentiation variational inference; Restricted Boltzmann machines; Condition monitoring; Anomalies detection; Predictive maintenance; Industry 40
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