Intelligent Fault Diagnosis and Predictive Maintenance of Rotating Machinery Using Artificial Intelligence and Vibration Analysis

Abstract

The integration of artificial intelligence (AI) with vibration analysis has revolutionized fault diagnosis and predictive maintenance strategies for rotating machinery in industrial applications. This empirical study investigates the application of AI-based techniques for intelligent fault diagnosis in rotating machinery, with particular emphasis on vibration signal analysis and machine learning algorithms. The research systematically analyzes data from various rotating equipment including bearings, gears, and motors to evaluate the effectiveness of AI methodologies in detecting and classifying fault patterns. Through comprehensive data collection and statistical analysis, this study examines multiple AI techniques including convolutional neural networks, support vector machines, and deep learning architectures applied to vibration signals. The empirical findings demonstrate that AI-enhanced diagnostic systems achieve superior accuracy rates ranging from 92% to 98.5% in fault classification compared to traditional methods. The study presents detailed comparative analysis of different AI algorithms, their computational efficiency, and diagnostic reliability under varying operational conditions. Results indicate that hybrid AI approaches combining feature extraction with deep learning provide optimal performance for real-time predictive maintenance applications. This research contributes to the advancement of intelligent maintenance strategies by providing empirical evidence of AI effectiveness in reducing unplanned downtime and extending machinery lifecycle through early fault detection and accurate diagnosis.