Contextually-Enhanced Deep Learning Prognostic Modeling for Predicting the Remaining Useful Life of Machines

Numerous factors, such as load, strain, stress, humidity, weather, and external conditions, inevitably have an impact on a machine’s endurance during continuous operation, which eventually results in wear and failure. Monitoring the health of a machine involves analyzing data related to tremors, temperatures, stress, strain, pressure, and more. This data serves as a foundation for predictive maintenance, a strategic approach aimed at forecasting potential breakdowns or failures, thereby enabling timely and cost-effective preventive measures. The primary goal is to minimize the necessity for expensive repairs and reduce periods of equipment unavailability. A critical aspect of prognostic protection lies in forecasting the durability of tools and assessing their Remaining Useful Life (RUL). This proactive approach to maintenance proves instrumental in overall equipment management. To enhance RUL estimation, this research advocates for the adoption of Deep Learning (DL) methods based on contextual information. Traditionally, prognostics tend to concentrate on a limited set of critical factors. However, our research highlights the significance of context-specific elements in boosting the effectiveness of learning algorithms. We use NASA-generated CMAPSS turbofan motor run-to-failure data to find contextual characteristics and use a variety of methods to include environmental information in model training. Adding contextual data to predictive models is the most important part of our approach. This gives us better prognostic accuracy and computational efficiency than other network architectures. In this work, contextual-based strategies such as contextual expansion, contextual adjustment, and contextual normalization were incorporated into learning models such as Long Short-Term Memory (LSTM), Deep Neural Network (DNN), and Linear Regression (LR). Combining various contextual-based strategies, such as contextual adjustment and contextual normalization, we developed hybrid contextual-based learning models. Among all models, the LSTM with the hybrid contextual strategy combining normalization and adjustment achieved the best results, with the lowest RMSE of 22.36 and highest R² of 0.77, outperforming traditional and benchmark models. These findings confirm the effectiveness of context-aware learning in predictive maintenance and establish a robust framework for future prognostics in industrial systems.