Development and Validation of Artificial Intelligence-Based Diagnostic Tools for Early Fault Detection in Mechanical Systems

Abstract

The Mechanical systems operating in industrial environments are subject to continuous wear, fatigue, and unexpected failure, leading to production losses and safety risks. Conventional fault diagnosis techniques rely on threshold-based monitoring or manual inspection, which often fail to detect early-stage anomalies. Artificial intelligence (AI)-based diagnostic tools offer a data-driven alternative capable of identifying subtle degradation patterns before catastrophic failure occurs. This study presents the development and experimental validation of AI-based diagnostic models for early fault detection in rotating mechanical systems. Multi-sensor vibration, acoustic, and thermal data were collected under varying operational conditions. Advanced feature extraction techniques and deep learning architectures were employed to classify fault types and assess system health. Model performance was evaluated using accuracy, sensitivity, and early detection capability. Results demonstrate that AI-based diagnostic tools significantly outperform traditional methods, enabling earlier fault identification and improved reliability in mechanical systems.