A Long Short-Term Memory-Based Deep Learning Approach for Predicting Remaining Useful Life of Downhole Drilling Sensors through Synthetic Data Generation

Summary Predictive maintenance such as remaining useful life (RUL), a cornerstone of modern industrial practices, faces the challenge of maintaining high prediction accuracy as sensors approach the end of their operational life. The nonlinear and complex nature of sensor degradation becomes more pronounced over time, leading to a decline in model performance just when precise predictions are most critical. In response to this challenge, synthetic data have proven to be a valuable tool in training machine learning models for predicting the RUL of downhole drilling sensors. By simulating realistic run-to-failure scenarios, synthetic data enable the modeling of diverse wear patterns and degradation processes, helping improve the accuracy of predictions, especially in the later stages of sensor life. This approach is essential in supporting Industry 4.0-driven maintenance strategies that rely on accurate and timely insights to optimize equipment reliability and minimize unplanned downtime. Leveraging a long short-term memory (LSTM) model has proved to effectively address this issue, demonstrating the lowest prediction error with a normalized root mean square error (nRMSE) of 7.60% and a mean absolute percentage error (MAPE) of 14.80%, outperforming extreme gradient boosting (XGBoost), which achieves an nRMSE of 8.28% and a MAPE of 17.30%. The LSTM’s ability to manage nonlinear degradation processes over time makes it particularly well-suited for predicting RUL in complex sensor systems. Furthermore, the inclusion of temperature-based features has been shown to significantly enhance predictive accuracy, particularly in modeling the intricate patterns that emerge as sensor health deteriorates. This approach aligns with Industry 4.0’s focus on smart and connected systems, where predictive maintenance ensures that machinery runs efficiently while minimizing downtime. The use of synthetic run-to-failure data opens the door to modeling various wear and failure scenarios, making it possible to generate more accurate predictions for different industrial tools and sensors.