Remaining Useful Life Estimation for Ball Bearings Using Feature Engineering and Extreme Learning Machine

作者：Jangwon Lee, Zhuoxiong Sun, Tai B. Tan, Jorge Méndez-Astudillo, Jesus Flores‐Cerrillo, Jin Wang, Qing He · 发表于：IFAC-PapersOnLine · 年份：2022 · DOI：10.1016/j.ifacol.2022.07.444 · 被引用次数：14 · 研究领域：Machine Fault Diagnosis Techniques、Reliability and Maintenance Optimization、Fault Detection and Control Systems

Rotating machines, such as pumps and compressors, are critical components in refinery and chemical plants used to transport fluids between processing units. Bearings are often the critical parts of rotating machinery, and their failure could result in economic loss and/or safety issues. Therefore, estimation of the remaining useful life (RUL) of a bearing plays an important role in reducing production losses and avoiding machine damage. Because bearing failure mechanisms tend to be complex and stochastic, data-driven RUL estimation approaches have found more applications. This work proposes a novel RUL estimation method based on systematic feature engineering and extreme learning machine (ELM). The PRONOSTIA dataset is used to demonstrate the effectiveness of the proposed method.