Improving Anomaly Identification by Comparing Machine Learning Classifiers

DOI

10.2991/978-94-6463-858-5_58How to use a DOI?

Keywords

Vibration Analysis; CNC machine; Predictive maintenance; SVM; Random Forrest

Abstract

In the context of Industry 4.0, AI is transforming smart manufacturing by allowing for accurate and efficient predictive maintenance strategies. This paper aims to apply AI method to vibration analysis in manufacturing systems, using an existing dataset from previous research. The dataset, which included vibration signals recorded from CNC machinery, was analyzed to determine patterns and anomalies that could be indicative of faults. Two supervised machine learning algorithms were used to classify the data and predict machine tool health i.e. SVM and Random Forest. Feature extraction, normalization and other pre-processing techniques were applied to enhance the quality of the dataset and build an optimal model. Model comparison was performed using accuracy, precision, recall and F1-score. The obtained results showed that SVM and Random Forest produced high classification accuracy, but with Random Forest performing little better than SVM because it can also handle non-linear relations as well as evaluate feature importance. These findings indicate the avenues through which AI-driven techniques can be harnessed to develop real-time vibration monitoring techniques for efficient fault diagnosis and downtime reduction in smart manufacturing.

Copyright

© 2025 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  - Sagar Wankhede
AU  - Pushkar Handi
PY  - 2025
DA  - 2025/11/04
TI  - Improving Anomaly Identification by Comparing Machine Learning Classifiers
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 666
EP  - 679
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_58
DO  - 10.2991/978-94-6463-858-5_58
ID  - Wankhede2025
ER  -