Abstract: AI-powered Predictive Maintenance (PdM) has emerged as a transformative strategy for optimizing industrial efficiency and asset management. This approach moves beyond traditional time-based or reactive maintenance by leveraging advanced Artificial Intelligence (AI) and Machine Learning (ML) algorithms to anticipate equipment failures before they occur. By continuously analyzing vast streams of real-time operational and sensor data (including vibration, temperature, pressure, and electrical signals) collected via the Industrial Internet of Things (IIoT), AI models can detect subtle anomalies and patterns indicative of impending failures. The core application involves building Remaining Useful Life (RUL) models to precisely forecast the time until a mechanical component requires servicing. The adoption of AI in PdM offers substantial benefits, including maximizing asset uptime, significantly reducing maintenance costs, extending equipment lifespan, and enhancing overall operational safety and efficiency in complex industrial environments. This paper explores the key AI techniques and their applications in monitoring and diagnosing industrial mechanical systems.
Title: AI Applications in Predictive Maintenance (PdM) for Industrial Mechanical Systems
Author: Mohammad F. Kh. A. Alenezi, Ebrahim Mohammad Almufarrej
International Journal of Mechanical and Industrial Technology
ISSN 2348-7593 (Online)
Vol. 13, Issue 2, October 2025 - March 2026
Page No: 1-3
Research Publish Journals
Website: www.researchpublish.com
Published Date: 23-October-2025
