Sequence-To-Sequence Health Index Estimation of Rolling Bearings with Long-Short Term Memory and Transfer Learning

• Published in: IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society pp. 1 - 5
• Main Authors: Berghout, Tarek, Benbouzid, Mohamed, Mouss, Leila-Hayet
• Format: Conference Proceeding
• Language: English
• Published: IEEE 13.10.2021
• Subjects: bearings; Employee welfare; LSTM; Memory management; PRONOSTIA; remaining useful life; Rolling bearings; Rotating machines; Training; Transfer learning; Transportation
• ISSN: 2577-1647
• DOI: 10.1109/IECON48115.2021.9589114

One of the main data-driven challenges when assessing bearing health is that training and test samples must be drawn from the same probability distribution. Indeed, it is difficult and almost rare to witness such a phenomenon in practical applications due to the constantly changing working conditions of rotating machines. In addition, collecting sufficient deterioration samples from the bearing life cycle is not possible due to the huge memory requirements and processing costs. As a result, accelerated life tests are believed to be the primary alternatives to such a situation. However, and unfortunately, the recorded samples always are subject to lack of real patterns. Therefore, in this paper, a transfer learning approach is performed to solve such kind of problem where PRONOSTICO dataset is used to assess the current procedures.