Data-driven framework for predictive maintenance in industry 4.0 concept

Abstract

Supporting the operation of the equipment at the operational stage with minimal costs is an urgent task for various industries. In the modern manufacturing industry machines and systems become more advanced and complicated, traditional approaches (corrective and preventive maintenance) to maintenance of complex systems lose their effectiveness. The latest trends of maintenance lean towards condition-based maintenance (CBM) techniques. This paper describes the framework to build predictive maintenance models for proactive decision support based on machine learning and deep learning techniques. The proposed framework implemented as a package for R, and it provides several features that allow to create and evaluate predictive maintenance models. All features of the framework can be attributed to one of the following groups: data validation and preparation, data exploration and visualization, feature engineering, data preprocessing, model creating and evaluation. The use case provided in the paper highlights the benefits of the framework toward proactive decision support for the estimation of the turbofan engine remaining useful life (RUL).