A Hybrid Machine-Learning Ensemble For Real-Time 4.0 Systems Anomaly Detection

DOI

10.2991/978-94-6463-471-6_128How to use a DOI?

Keywords

SCADA; Embedded Systems; AI Algorithms; Hybrid Machine-Learning; OCSVM; and Auto encoder (AE)

Abstract

Massive amounts of data are created and collected by data acquisition systems, including SCADA and embedded systems in industrial machines. The data may be analysed by AI algorithms to gain a better understanding of the process and identify any irregularities in the machines. This is a major benefit of Industry 4.0. This paper will examine predictive maintenance as a process in industry and demonstrate how it may make full use of technologies from Industry 4.0. Using a weighted average, our method integrates Three Machine Learning models: Auto encoder (AE), One-Class Support Vector Machine (OCSVM), and Local Outlier Factor (LOF). This allows us to discover anomalies in real-time. Anomaly detection will be enhanced as a result. Improve real-time anomaly detection using the suggested hybrid machine-learning ensemble pipeline by combining the outputs of three ML models: auto encoder (AE), one-class support vector machine (OCSVM), and local outlier factor (LOF).

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - V. S. A. Chandramouli
AU  - B. Ganga Bhavani
AU  - S. Divyateja
AU  - P. Anitha
AU  - N. R. D. S. S. Srinivas
AU  - P. L. Kumar
PY  - 2024
DA  - 2024/07/30
TI  - A Hybrid Machine-Learning Ensemble For Real-Time 4.0 Systems Anomaly Detection
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 1335
EP  - 1343
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_128
DO  - 10.2991/978-94-6463-471-6_128
ID  - Chandramouli2024
ER  -