AI and cloud computing integration for fault detection using deep learning models and autoencoders

Cloud computing with AI to apply fault detection employing deep learning networks and autoencoders to industrial systems. Constructing a scalable real-time fault detection system with sensor data in an unsupervised manner using the fact that fault data usually tends to be insufficient in real industrial environments. The suggested framework makes use of autoencoders to learn normal system behavior, while anomalies are determined by comparing the reconstruction error of the incoming data. A cloud-based architecture supports the system to process large amounts of sensor data, making it scalable and fast in processing data. Experimental validation on real-world datasets proved the system to be effective, as it attained 99.35% accuracy, 99.49% precision, 99.48% recall, and an F1-score of 99.49%. Unlike conventional rule-based or centralized fault detection approaches, the proposed framework integrates cloud computing with autoencoder-based unsupervised learning to enable scalable and real-time fault detection from high-volume sensor data. This integration allows accurate identification of complex and previously unseen fault patterns while minimizing dependence on labeled fault data. Cloud-based processing further enhances scalability, computational efficiency, and detection accuracy, making the proposed approach suitable for large-scale industrial monitoring and predictive maintenance applications. Cloud computing integration helps with continuous observation and supports mass fault detection. The results validate that the proposed approach can be implemented in predictive maintenance in industries for improving the reliability of systems, minimizing downtime, and reducing operating costs. Future development will be aimed at increasing adaptability through incremental learning, extending it to multi-sensor systems, and integrating explainable AI (XAI) for improved decision-making.