Unsupervised Learning Applications for Predictive Maintenance in Consumer Electronics and Intelligent Energy Networks

This study presents a novel framework integrating Variational Autoencoders (VAE) and Particle Swarm Optimization (PSO) to enhance predictive maintenance in consumer electronics and intelligent energy networks. The primary objective is to address challenges such as early fault detection, downtime reduction, and cost efficiency by leveraging unsupervised learning for anomaly detection and optimization techniques for model improvement. The proposed approach utilizes VAE for feature extraction and anomaly detection by learning compact latent representations of system behavior, while PSO optimizes the model’s parameters to enhance accuracy and efficiency. Experimental results demonstrate that the VAE + PSO framework outperforms traditional machine learning and existing predictive maintenance methods in key metrics, achieving 94% accuracy, a 20% reduction in maintenance costs, and a 5% false positive rate. These findings underscore the potential of combining unsupervised learning with optimization techniques for predictive maintenance in increasingly complex smart systems. The framework’s scalability and adaptability make it a promising solution for real-time applications in industrial IoT, autonomous networks, and edge computing. Future directions include exploring enhanced scalability and integration with other decision-making algorithms.