Deep Learning-Based Anomaly Detection in Occupational Accident Data Using Fractional Dimensions

Abstract

This study examines the effectiveness of Convolutional Autoencoder (CAE) and Variational Autoencoder (VAE) models in detecting anomalies within occupational accident data from the Mining of Coal and Lignite (NACE05), Manufacture of Other Transport Equipment (NACE30), and Manufacture of Basic Metals (NACE24) sectors. By applying fractional dimension methods—Box Counting, Hall–Wood, Genton, and Wavelet—we aim to uncover hidden risks and complex patterns that traditional time series analyses often overlook. The results demonstrate that the VAE model consistently detects a broader range of anomalies, particularly in sectors with complex operational processes like NACE05 and NACE30. In contrast, the CAE model tends to focus on more specific, moderate anomalies. Among the fractional dimension methods, Genton and Hall–Wood reveal the most significant differences in anomaly detection performance between the models, while Box Counting and Wavelet yield more consistent outcomes across sectors. These findings suggest that integrating VAE models with appropriate fractional dimension methods can significantly enhance proactive risk management in high-risk industries by identifying a wider spectrum of safety-related anomalies. This approach offers practical insights for improving safety monitoring systems and contributes to the advancement of data-driven occupational safety practices. By enabling earlier detection of potential hazards, the study supports the development of more effective safety policies, and could lead to substantial improvements in workplace safety outcomes.

Keywords

• anomaly detection
• fractional dimension
• deep learning
• occupational safety