Physics-informed remaining useful life prediction of rolling bearings under variable speed using vibration envelope features and adaptive maintenance thresholds

A Comprehensive Review of Rolling Bearing Life Prediction: From Fatigue Life Model to Data-Driven Remaining Useful Life Prognostic

Rolling bearings serve as core rotating components in high-end equipment such as aerospace systems, wind turbines, and high-speed electric multiple units, and their service life directly affects the operational reliability and service life of the host machinery. To clarify the research landscape of rolling bearing life prediction, summarize existing prediction techniques, and identify future development trends, this paper systematically reviews the major research advances in this field. The review first traces the evolution of bearing life models, with particular emphasis on the roles of key influencing factors, including stress thresholds, material defects, and lubrication conditions, in their development. Second, it presents a comparative analysis between conventional life calculation methods and those that account for dynamic variations in lubrication conditions, thereby revealing the influence patterns and underlying mechanisms through which surface topography and oil-film characteristics affect fatigue life. Third, it discusses methods for assessing bearing system life, with special attention given to accelerated life testing techniques and bearing condition monitoring approaches. Finally, it summarizes the state of the art in data-driven bearing life prediction and identifies online sensing of lubrication states, system-level reliability design, and improvements in the interpretability and robustness of AI-based prediction models as important future research directions in rolling bearing life prediction.

Prediction and Classification of Residual Service Life in Wind Turbine Bearings Under Variable Speed Conditions Using Hybrid Machine Learning Models

A hybrid methodology for classifying degradation stages and estimating a relative RUL-related degradation indicator for bearings is proposed by integrating synthetic data modeling, feature selection, and a combined unsupervised–supervised learning approach, offering a reliable and scalable solution for predictive maintenance and assessment of degradation progression in wind turbine bearings.

The Impact of Varying Speed on Remaining Useful Life Estimation for Rolling Element Bearing

Most data-driven bearing remaining useful life (RUL) estimation studies assume constant rotational speed, which does not reflect real industrial operating conditions. In applications such as electric vehicle drivetrains, bearings operate under continuously varying speed profiles that modulate vibration signal characteristics and obscure degradation signatures, making RUL estimation significantly more challenging. This study presents an experimental investigation of the effect of speed non-stationarity on deep learning-based bearing RUL estimation. Two run-to-failure accelerated life tests were conducted on SKF 6203-2RSH/C3 deep groove ball bearings: one under a constant rotational speed and one under a time-varying speed profile derived from the Worldwide Harmonised Light Vehicles Test Procedure (WLTP). Average speed and radial loading were kept identical across both experiments to isolate the effect of speed variability. A 1D residual convolutional neural network (CNN), a gated recurrent unit (GRU) network and an attention-augmented bidirectional gated recurrent unit network (Att-BiGRU) were evaluated under four training-test scenarios covering both within-condition and cross-condition configurations. Within-condition results show that all models can learn meaningful degradation patterns even under non-stationary conditions when training and test data are matched, yielding RMSE values of 12.93, 16.58 and 12.37 cycles for the CNN, GRU and Att-BiGRU respectively under the WLTP regime. These results demonstrate that variable speed operation represents the general case for industrial bearing prognostics and that dedicated strategies at the feature extraction, model architecture, and training levels are required to develop reliable RUL estimators for real-world applications.

Remaining Useful Life Prediction for Rolling Bearings by Integrating Degradation Assessment with DK-Mamba

Remaining useful life (RUL) prediction of rolling bearings is essential for ensuring the safe operation and condition-based maintenance of rotating machinery. To address unreliable degradation-onset identification and insufficient joint modeling of trend and detail components in non-stationary degradation signals, this paper proposes a two-stage framework that integrates degradation assessment with the DK-Mamba network. First, a multi-feature health indicator is constructed, and a dual-branch persistent validation strategy (DPV-FPT) is developed to robustly identify the first prediction time (FPT). Second, a multi-scale dynamic decomposition module is employed to adaptively separate the degradation sequence into trend and detail components. The Mamba architecture is then used to capture long-range temporal dependencies in the trend branch, while wavelet-based soft-threshold shrinkage is introduced to suppress noise-like high-frequency disturbances and JacobiKAN is employed to enhance the nonlinear representation of localized impulsive degradation features in the detail branch. Finally, a cross-attention mechanism is employed to adaptively fuse the trend and detail representations for RUL prediction. Experiments on the PHM2012 and IMS datasets demonstrate that the proposed method improves FPT identification reliability and achieves accurate and robust RUL prediction under complex degradation patterns and varying operating conditions.

Machine Learning and Vibration-Based Method for Anti-Friction Bearing Fault Severity Estimation

The proposed method addresses both fault detection and degradation assessment for anti-friction bearings using simple vibration-based parameters based on rotor and bearing dynamics, providing a practical framework for predictive maintenance in industrial applications.

Vibration and time based feature engineering for comprehensive gear health