Industrial Remaining Useful Life Prediction by Partial Observation Using Deep Learning With Supervised Attention

Effective and reliable machinery health assessment and prognostic methods have been highly demanded in modern industries. In the past years, promising prognostic results have been achieved by the intelligent data-driven approaches. However, the existing methods generally rely on the availability of the complete system information. In the real industries, due to practical restrictions of mechanical structure, sensor installation etc., the collected data may only cover partial system health information, which is denoted as partial observation problem. The existing data-driven methods are basically less effective in such scenarios with data incompleteness and disturbances. In order to address this issue, a deep learning-based remaining useful life prediction method is proposed in this article, where supervised attention mechanism is introduced. The informative data with more significant degradation features can be focused on for prognostics, and the data with nondiscriminative features can be ignored. A cutting wheel degradation image dataset is contributed to validate the proposed method, which is prepared from the real industrial manufacturing process. The experimental results suggest the proposed method offers an effective and promising approach on the prognostic problems with partial observations.