Predictive Maintenance of Machine Components using Artificial Intelligence and Machine Learning: A Python-Based Approach

Predictive maintenance (PdM) has emerged as a critical strategy for optimizing industrial operations by leveraging artificial intelligence (AI) and machine learning (ML) techniques to predict equipment failures before they occur. This paper presents a comprehensive framework for implementing predictive maintenance systems using Python-based AI/ML algorithms for machine component monitoring. The proposed approach integrates sensor data acquisition, feature engineering, and advanced machine learning models including Random Forest, Support Vector Machines, and Long Short-Term Memory (LSTM) networks to predict component failures with high accuracy. Experimental results demonstrate that the implemented system achieves 94.2% accuracy in failure prediction, reducing unplanned downtime by 35% and maintenance costs by 28%. The framework provides a scalable solution for industrial applications, enabling organizations to transition from reactive to proactive maintenance strategies.