AI-Enhanced Predictive Maintenance for Industrial Robotics using Sensor Data Analysis and Machine Learning

Abstract

Modern manufacturing is progressing towards Industry 4.0 and Robotics 4.0, which puts the industrial robots in the focus of contemporary manufacturing. These sophisticated robots keep on producing multi-sensor rich data, which depicts their internal kinematics, forces, torques, and joint dynamics, which is useful in detecting anomalies that may result in failures in time. However, even with access to these types of data, fixed-interval or reactive maintenance plans continue to be implemented by many industries, which results in unwarranted downtime and expensive interruption. These gaps, combined with the necessity to have the predictive maintenance schemes that are reliable and capable of converting the raw sensor images into actionable intelligence based on machine learning. We focused on creating an AI-enhanced predictive maintenance model of industrial robotics, based on internal robot states and machine learning algorithms. The paper concentrates on data pre-processing, feature engineering, anomaly detection, Remaining Useful Life (RUL) prediction, and designing a data-driven maintenance decision-support system that includes and operates synchronized robot sensor streams three tools of kinematics, joint positions, and force/torque measurements then proceed to extract time-domain features, and correlation-based features to identify patterns of early degradation. Exploration of sensor relationships and structural separability of normal and abnormal conditions was performed by use of Principal Component Analysis (PCA) and correlation matrices. Logistic Regression, Support Vector Machine (SVM), Random Forest and XGBoost were four classical machine learning models trained and tested on chronological splits. Accuracy, precision, recall, F1-score, ROC-AUC, PR-AUC were utilized as a measurement of model performance. A decision-support system was subsequently developed to transform predictive results to a form of maintenance recommendations.

The patterns in the robot signals are well established and consistent between normal behavior where random Forest and XGBoost performed better than any of the remaining models. The study proves scientific soundness of applying the classical machine learning models specifically the Random Forest and the XGBoost on predictive maintenance in industrial robotics. It established internal robot data when adequately preprocessed and engineered provides strong predictive capability in detecting faults early and estimating their life. The decision-support framework suggested is a feasible avenue that industries can use to minimize production downtimes, enhance their safety, and enhance the transition to Robotics 4.0. This paper will add fresh empirical data, methodological soundness and practical solutions to the expanding research on AI-enhanced maintenance in intelligent manufacturing settings.

KEYWORDS: Predictive Maintenance, Industrial Robotics, Machine Learning, Sensor Data Analysis, Remaining Useful Life (RUL) Prediction, Industry 4.0/ Robotics 4.0