A hybrid framework for data-driven predictive maintenance: probabilistic RUL estimation and early failure signaling via control charts

Abstract

The purpose of this study is to develop and validate a robust framework for data-driven predictive maintenance (PdM) that estimates the remaining useful life (RUL) of equipment and signals the optimal time to initiate maintenance activities. By integrating statistical modeling and machine learning techniques, the proposed framework aims to minimize unplanned downtimes, reduce maintenance costs and enhance operational efficiency. It addresses critical gaps in existing methods by providing real-time condition monitoring and predictive alerts, enabling maintenance personnel to make informed decisions and optimize maintenance scheduling. This study introduces a data-driven predictive maintenance framework designed to estimate the RUL of equipment and provide timely signals for initiating maintenance tasks. The framework utilizes a combination of statistical modeling and machine learning algorithms. A Weibull distribution with time-varying parameters is employed to model the RUL, while random forest and exponentially weighted moving average (EWMA) control charts are integrated for failure prediction and maintenance signaling. The methodology is validated using a synthetic dataset that simulates real-world scenarios, enabling robust evaluation of the proposed approach in terms of accuracy and reliability. The findings demonstrate that the proposed predictive maintenance framework effectively estimates the RUL of equipment with high accuracy and provides timely signals for initiating maintenance tasks. Validation on a synthetic dataset reveals that the framework consistently predicts failure probabilities and generates alerts well in advance, allowing sufficient time for maintenance planning. The integration of Weibull distribution modeling and Random Forest classifiers enhances the reliability of predictions, while the use of EWMA control charts ensures robust monitoring of failure probabilities. These results highlight the framework's potential to reduce downtimes and maintenance costs. This study introduces a novel data-driven predictive maintenance framework for accurately estimating the RUL of equipment and providing timely signals for maintenance actions. Unlike existing methods, the proposed approach integrates a probabilistic model with machine learning techniques, leveraging time-varying Weibull distributions and advanced statistical tools for robust predictions. The framework not only improves failure prediction accuracy but also enhances maintenance planning by signaling appropriate times for action. This contributes to reducing downtime and costs while increasing operational efficiency, offering a significant advancement for Industry 5.0 and smart manufacturing applications.