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Opinion - International Research Journal of Engineering Science, Technology and Innovation ( 2025) Volume 11, Issue 1

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Artificial Intelligence in Predictive Maintenance for Industrial Systems

Rahul Deshpande*
 
Department of Mechanical and Automation Engineering, Zenith Polytechnic University, India
 
*Corresponding Author:
 Rahul Deshpande, Department of Mechanical and Automation Engineering, Zenith Polytechnic University, India, Email: bhatt.soundrya@gmail.com

Received: 01-Mar-2025, Manuscript No. irjesti-25-169521; Editor assigned: 03-Mar-2025, Pre QC No. irjesti-25-169521(PQ); Reviewed: 17-Mar-2025, QC No. irjesti-25-169521; Revised: 21-Mar-2025, Manuscript No. irjesti-25-169521(R); Published: 29-Mar-2025

Abstract

Predictive maintenance (PdM) is a proactive approach that utilizes data analytics and machine learning to forecast equipment failures before they occur. This method contrasts with reactive maintenance, where repairs happen after breakdowns, and preventive maintenance, which follows fixed schedules regardless of equipment condition (Clancy S 2008). Artificial intelligence (AI) enables PdM to analyze sensor data in real time, detect anomalies, and recommend corrective actions. The result is reduced downtime, optimized asset utilization, and lower maintenance costs. AI’s role in PdM is expanding across industries such as manufacturing, energy, aviation, and transportation. This article examines AI-driven predictive maintenance systems, their architecture, challenges, and future prospects.

INTRODUCTION

Predictive maintenance (PdM) is a proactive approach that utilizes data analytics and machine learning to forecast equipment failures before they occur. This method contrasts with reactive maintenance, where repairs happen after breakdowns, and preventive maintenance, which follows fixed schedules regardless of equipment condition (Clancy S 2008). Artificial intelligence (AI) enables PdM to analyze sensor data in real time, detect anomalies, and recommend corrective actions. The result is reduced downtime, optimized asset utilization, and lower maintenance costs. AI’s role in PdM is expanding across industries such as manufacturing, energy, aviation, and transportation. This article examines AI-driven predictive maintenance systems, their architecture, challenges, and future prospects (Wilson RC et al., 2013).

DESCRIPTION

AI-powered PdM systems integrate Internet of Things (IoT) sensors, cloud computing, and advanced algorithms (Carthew RW et al., 2009). Sensors collect data on vibration, temperature, pressure, and acoustic emissions from machinery. AI algorithms—ranging from supervised learning models like random forests to deep learning architectures—process this data to detect deviations from normal operating conditions (Borges F et al., 2015). These systems often incorporate digital twins, which are virtual models of physical assets, enabling simulation and scenario testing. Industries such as wind energy use AI PdM to anticipate gearbox failures months in advance, avoiding costly downtime. In aviation, AI analyzes flight data to identify maintenance needs, enhancing safety and efficiency (Sinha SK et al., 2010).

DISCUSSION

AI-based PdM offers significant advantages over traditional approaches (Obbard DJ et al., 2009). For example, automotive manufacturing plants have reported up to 25% reductions in unplanned downtime after adopting AI PdM systems (Li C et al., 2019). Energy companies achieve extended asset lifespans and reduced operational costs through predictive insights. However, challenges include data quality issues, cybersecurity concerns, and the high initial investment required for implementation (Williams M et al., 2004). AI models must be trained on large, representative datasets to ensure accuracy. Data privacy regulations, such as GDPR, may limit the extent of data sharing necessary for model improvement (Brantl S et al., 2002). Moreover, the success of PdM relies on skilled personnel who can interpret AI outputs and integrate them into maintenance workflows. Future trends involve edge computing to process data locally, reducing latency and bandwidth requirements (Agrawal N et al., 2003).

CONCLUSION

The fusion of AI with predictive maintenance marks a significant advancement in industrial reliability and efficiency. While challenges in cost, data management, and cybersecurity persist, the long-term benefits far outweigh the barriers. Organizations that adopt AI PdM can expect improved operational performance, cost savings, and enhanced safety. As AI models evolve and edge computing becomes mainstream, predictive maintenance will likely become the standard in industrial asset management.

REFERENCES