Abstract

Machine health monitoring and management are essential improvements that must be considered in the industry toward smart manufacturing. Intelligent prognosis and health management (PHM) systems have demonstrated remarkable capabilities for industrial use and, consequently, have become active research areas in the last several decades. Predictive Maintenance (PM) generally predicts faults or breakdowns in a deteriorating system to optimize maintenance efforts by evaluating the system's status using historical data. In this strategy, the Remaining Useful Life (RUL) of the components is anticipated using characteristics, which typically include sensors and operational profiles. This research aims to evaluate the possibility of predicting the RUL of a system based on sensor data by deploying an attention-based deep learning model. RUL prediction based on the attention mechanism is a relatively new approach with promising results. One advantage of this approach is that it can be useful for interpreting the results and understanding the underlying factors contributing to the RUL. Applying an attention mechanism to find temporal dependencies also shows improvement in model performance by detecting the most important part of the sequences to be passed to the prediction model. Our proposed model has shown a noticeable impact on the performance of the neural network architecture from the attention mechanism added to the pipeline by keeping the model light in terms of computational resources and training time. The proposed model clearly shows the attention mechanism's high impact on predicting sequential data. This technique can also be used in more complex ensemble-based architectures to improve performance.