Predictive Maintenance for Industrial Equipment: Using XGBoost and Local Outlier Factor with Explainable AI for analysis

• Published in: 2024 14th International Conference on Cloud Computing, Data Science & Engineering (Confluence) pp. 25 - 30
• Main Authors: Ghadekar, Premanand, Manakshe, Aman, Madhikar, Sarthak, Patil, Sushrut, Mukadam, Mehvish, Gambhir, Tejas
• Format: Conference Proceeding
• Language: English
• Published: IEEE 18-01-2024
• Subjects: Explainable AI; Industrial materials; Local Outlier Factor; Merging; Prediction algorithms; Predictive maintenance; Predictive models; Productivity; Reliability; Task analysis; XGBoost
• DOI: 10.1109/Confluence60223.2024.10463280
• EISSN: 2766-421X

In industrial operations, the need to minimize downtime and enhance productivity has produced the need for predictive maintenance techniques. Using artificial intelligence (AI) in this domain has revolutionized maintenance practices, but the lack of transparency of many AI models has obstructed their widespread acceptance and trustworthiness. This research paper explores the application of XGBoost and Local Outlier Factor algorithms in the domain of Predictive Industrial Maintenance for industrial materials. Using these advanced machine learning techniques, this research aims to predict equipment failures and improve the overall reliability of industrial processes. In addition, this research employs Explainable AI methods to provide clear and interpretable insights into the predictive models' decision-making processes. By combining the power of XGBoost and Local Outlier Factor with explainability. In this study, for predictive classification, the XGBoost gave an F1 score of 96% and for early prediction Local Outlier Factor gave an F1 score of 94% this research also explained the impact of features on Output class using the SHAP model. This research not only enhances predictive accuracy but also ensures transparency, enabling users to make informed decisions for timely maintenance and system optimization.