Keeping it rolling
Osaka, Japan - Scientists from the Institute of Scientific and Industrial Research, and NTN Next Generation Research Alliance Laboratories at Osaka University developed a machine learning method that combines convolutional neural networks and Bayesian hierarchical modeling to precisely predict the remaining useful life of rolling bearings. This work may lead to new industrial monitoring methods that help manage maintenance schedules and maximize efficiency and safety under defect progression.
A rolling bearing consists of two rings separated by rolling elements (balls or rollers). Because of the ease of rolling, the rings can rotate relative to each other with very little friction. Rolling bearings are essential to almost all automated machinery with rotating elements. The bearings ...










