Journal Bearing

Journal bearings are crucial rotating machine components whose reliable operation is essential for various industries. Current research heavily emphasizes developing advanced diagnostic and prognostic tools, focusing on machine learning techniques like convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and survival analysis models to predict bearing failures and remaining useful life (RUL) from vibration data, even under noisy or time-varying conditions. These efforts aim to improve predictive maintenance strategies, reducing downtime and operational costs. Furthermore, probabilistic approaches are being explored to better account for uncertainties in bearing measurements and improve the accuracy of shaft center localization.