Interpretable Vae-based Predictive Modeling For Enhanced Complex Industrial Systems Dependability In Developing Countries.

Rapid industrial growth in developing countries necessitates robust maintenance, with predictive maintenance (PdM) offering a vital solution to minimize downtime and costs. However, complex industrial systems and the acute scarcity of labeled data, particularly in African contexts, present significant implementation challenges for traditional PdM approaches. This research proposes a novel predictive maintenance approach utilizing a Variational Autoencoder (VAE), specifically designed to address data scarcity and enhance interpretability in complex industrial systems within developing countries. The VAE is trained on real-world operational data, learning intricate system patterns. Its interpretability is a core feature, achieved through visualization and analysis of the latent space, providing deeper insights into system behavior. The VAE model demonstrates strong and consistent performance in anomaly detection and data reconstruction, evidenced by low Mean Squared Error (MSE) and favorable R² values, and rigorously validated through cross-validation, which confirms its robustness and generalizability. This highlights its capacity to accurately model complex system dynamics across varied data subsets. This interpretable VAE model offers a powerful and promising predictive maintenance solution for enhancing the dependability 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.