Interpretable VAE-based predictive modeling for enhanced complex industrial systems dependability in developing countries

Abstract

Rapid industrial growth in developing countries requires robust maintenance, and predictive maintenance (PdM) is a key solution to minimize downtime and costs. However, complex industrial systems and the acute scarcity of tagged data, particularly in African contexts, pose significant implementation challenges for traditional PdM approaches. This research proposes a novel predictive maintenance approach using a Variational Autoencoder (VAE) specifically designed to address data scarcity and improve interpretability in complex industrial systems in developing countries. The VAE is trained on real operational data and learns complex system patterns. Its interpretability is a key feature, achieved through visualization and analysis of latent space, providing deeper insight into system behavior. The VAE model demonstrates strong and consistent performance in anomaly detection and data reconstruction, as evidenced by low Mean Squared Error (MSE) and favorable R² values, and is rigorously validated through cross-validation, confirming its robustness and generalizability. This underscores its ability to accurately model complex system dynamics across diverse data subsets. This interpretable VAE model offers a powerful and promising predictive maintenance solution for improving the reliability of complex industrial systems in developing countries. By enabling early anomaly detection, synthetic data generation, and improved decision making, this approach has the potential to significantly contribute to the growth and sustainability of industries in these regions through reduced downtime and optimized resource utilization.