AI-Driven Predictive Maintenance Systems for Industrial Equipment: A Machine Learning Approach

This paper presents a comprehensive framework for AI-driven predictive maintenance systems for industrial equipment using advanced machine learning techniques. As industrial machinery becomes increasingly complex and interconnected, traditional maintenance approaches are proving inadequate in preventing costly downtime and optimizing equipment lifespan. We propose a multi-stage predictive maintenance framework incorporating data collection, preprocessing, feature engineering, model selection, and deployment. Our approach utilizes a hybrid ensemble model combining Convolutional Neural Networks (CNNs) for vibration signal processing, Long Short-Term Memory (LSTM) networks for temporal pattern recognition, and gradient boosting algorithms for failure prediction. The proposed methodology was implemented in a large manufacturing facility with over ۱۲۰ industrial machines across diverse production lines. Results demonstrate a ۷۸% reduction in unexpected downtime, ۳۴% decrease in maintenance costs, and ۲۳% improvement in equipment lifespan compared to traditional preventive maintenance strategies. Additionally, the system achieved ۹۲% accuracy in predicting equipment failures up to ۷۲ hours in advance, with ۸۹% precision and ۸۶% recall. This research contributes to the field by demonstrating the efficacy of hybrid machine learning models in industrial maintenance and providing a scalable framework for implementation across different manufacturing environments.

کلیدواژه ها:

Predictive Maintenance ، Machine Learning ، Industrial IoT ، Condition Monitoring ، Fault Diagnosis ، Industry ۴.۰