LAUSR

Due to the rise of Industry 4.0, machinery health prognosis has become one of the primary objectives of machine maintenance. Long-term operating machines gradually age over time, resulting in machine degradation and lower production yields. In general, the entire life cycle of a machine is from health to degradation to fault states. Once a machine breaks down, it may increase production costs and cause serious safety hazards. To prevent the machine from operating in a fault state, a variety of sensors are applied for machine health monitoring. Subsequently, the collected sensor data are fed into a degradation model, which is used to evaluate the machine degradation level. As the machine degradation process changes continuously over time, the features in the transition region between two adjacent condition states are nearly identical. Similar features lead to a poor degradation model performance in the transition region. In this study, a novel label-smoothing method is proposed to improve model performance in the transition region. To enhance the machine degradation model accuracy, the proposed method consists of four major processes: preprocessing, data label smoothing, model training, and postprocessing. The developed model is verified using a bearing run-to-failure dataset and a tool wear dataset, achieving a prediction accuracy of over 90%. The experimental results demonstrate that the proposed method outperforms other existing peer methods, especially in the transition region.