Development of an intelligent predictive maintenance system using sensor data and neural networks

The rapid advancement of Industry 4.0 has emphasized the importance of intelligent condition monitoring systems that can anticipate equipment failures before they disrupt operations. This paper presents a novel predictive maintenance framework that integrates real-time sensor data with a hybrid deep learning architecture to assess equipment health and proactively detect early-stage faults. The proposed system processes multivariate time-series inputs—collected from vibration, temperature, and pressure sensors—and converts them into structured segments suitable for learning temporal and spatial patterns. A convolutional neural network is used to extract localized features from sensor signals, followed by a long short-term memory network that captures sequential dependencies across time. This dual-stage architecture is optimized for dynamic fault detection and failure probability scoring. The model is trained on a labeled dataset using binary cross-entropy loss and validated using precision, recall, and F1-score metrics. Experimental results demonstrate that the framework offers high predictive accuracy, robust generalization, and practical applicability across industrial environments. This study provides an adaptable and scalable solution for smart maintenance decision-making systems.