Roller bearings are lubricated with oils or greases containing several additives to reduce wear and corrosion. Given that almost 50% of bearing failures arise from abrasive/adhesive wear and moisture-induced corrosion, the combined role of EP/AW additives and corrosion inhibitors (CIs) is crucial. However, their polar nature indicates potential antagonism when CIs are combined with EP/AW-containing oils. The impact of simultaneous EP/AW and CI use on corrosion resistance remains unknown. This study presents a novel corrosion protection test to evaluate this interaction. Furthermore, studies on other additive combination demonstrate the importance of the ratio between additives for optimum functionality. Therefore, the effect of the CI ratio to EP/AW on wear and corrosion protection is also being studied. The results clearly recommend the addition of 0.5 wt% CI when using 1.0 wt% EP/AW in order to achieve a good compromise between wear and corrosion protection.

The selection of penalty hyperparameters is a critical aspect in Nonnegative Matrix Factorization (NMF), since these values control the trade-off between reconstruction accuracy and adherence to desired constraints. In this work, we focus on an NMF problem involving the Itakura-Saito (IS) divergence, which is particularly effective for extracting low spectral density components from spectrograms of mixed signals, and benefits from the introduction of sparsity constraints. We propose a new algorithm called SHINBO, which introduces a bi-level optimization framework to automatically and adaptively tune the row-dependent penalty hyperparameters, enhancing the ability of IS-NMF to isolate sparse, periodic signals in noisy environments. Experimental results demonstrate that SHINBO achieves accurate spectral decompositions and demonstrates superior performance in both synthetic and real-world applications. In the latter case, SHINBO is particularly useful for noninvasive vibration-based fault detection in rolling bearings, where the desired signal components often reside in high-frequency subbands but are obscured by stronger, spectrally broader noise. By addressing the critical issue of hyperparameter selection, SHINBO improves the state-of-the-art in signal recovery for complex, noise-dominated environments.

An adaptive sparse Bayesian neural network-informed incremental prediction method for remaining useful life of rolling bearings

Uncertainty aware federated averaging approach for privacy secured collaborative remaining useful life prediction of rolling element bearing

Rotating machinery often operates under varying working conditions, which poses significant challenges to achieving reliable bearing fault diagnosis using traditional deep learning-based models. To enhance the diagnostic performance for rolling bearings across diverse operational conditions and noisy environments, a data-selective multiscale dual transfer network (DSMDTN) with a data selector (DS), multiscale concatenation U-Net (MCU-Net), and dual classifier (DC) is proposed. The DS module employs a comprehensive scoring mechanism that integrates math, entropy, and anomaly scores to selectively identify high-quality source samples for model training. Meanwhile, the MCU-Net module incorporates gated convolutional (gated-conv) blocks and convolutional blocks to extract multiscale domain-invariant features and dynamically adjust feature importance. In addition, the DC module comprises separate source and target classifiers that jointly minimize distribution discrepancy and classification loss. The effectiveness of the proposed DSMDTN is validated through experiments on the public Case Western Reserve University (CWRU) dataset and the proprietary PT dataset collected from a PT500mini test bed. The experimental results demonstrate that DSMDTN achieves higher accuracy and exhibits stronger transfer capability compared to several state-of-the-art intelligent models across various transfer tasks and under different noise levels.

Remaining useful life prediction for rolling bearings using least squares support vector regression based on optimal mode decomposition

FV-PCBA Net: a fault diagnosis model for rolling bearings with hybrid preprocessing, parallel feature extraction and cross-attention mechanism

Octave cmsgram: an effective tool for acoustic fault diagnosis of rolling bearings

Fault diagnosis of rolling bearings based on mechanism-guided deep subdomain adaptation method

Multiscale improved phase entropy and its application in fault diagnosis of rolling bearings

The present study investigates the structural performance of uniformly tapered hollow roller (UTHR) and uniformly tapered layered hollow roller (UTLHR) bearings with varying hollowness levels using finite element analysis (FEA) and experimental validation. A comprehensive numerical investigation was conducted for hollowness levels ranging from 30% to 80% to evaluate maximum deflection, bending stress, von Mises stress, contact pressure, endurance-limit loading, and radial stiffness. The finite element results identified an optimum hollowness range of approximately 31-40%, where stress redistribution was achieved without excessive loss of stiffness. The analytical study predicted applied radial load of 30 kN, with a deviation of 0.1412%, which is validating the theoretical formulations. Moreover, simulations results realized reduced stress concentrations and optimized stiffness at optimum hollowness. The layered hollow roller configurations dominate over single hollow roller design in terms of lower contact pressure and improved stress distribution. The experimentally measured static failure loads were 53.01 kN and 76.17 kN for the UTHR and UTLHR bearings, respectively. The experimental results showed excellent agreement with the finite element predictions, with deviations below 2.5%. Accordingly, the effective similarities between the predicted and experimental results shows the reliability of the adopted modelling approach and highlights the structural advantages of layered hollow rollers within an optimized hollowness range.

Rolling bearings in agricultural machinery operate under pronounced operating-condition shifts, such as speed and load fluctuations and contamination-related noise, which often induce a distribution mismatch between training and deployment signals. This work studies bearing fault diagnosis under a source-only, single-source domain generalization (DG) setting, where the model is trained and selected using only source-domain data, and samples from the target operating condition or target dataset are not used for training, validation, hyperparameter selection, band-pass selection, or early stopping. We formulate cross-condition robustness as a semantic consistency problem between two complementary representations: an analytic mechanism-oriented representation emphasizing impact-related resonance demodulation, and a data-driven temporal representation learned from raw waveforms. A dual-path framework is developed accordingly. The analytic path learns a differentiable soft band-pass mask to localize an informative resonance band and constructs an impulse-oriented descriptor from statistics of the band-pass signal, its Hilbert envelope, and squared-envelope energy. The temporal path encodes normalized raw segments using a lightweight dilated one-dimensional convolutional network with temporal-attention pooling. The two embeddings are fused by a sample-wise gate, with an entropy penalty used to discourage near-uniform averaging. This design aims to improve cross-condition generalization by using the analytic path as a mechanism-oriented semantic anchor, constraining the temporal path through cross-view agreement, and allowing the fused representation to adapt to sample-dependent reliability changes. To reduce representation drift under regime changes, the two views are aligned using a bidirectional InfoNCE objective with a learnable temperature that adapts similarity scaling across operating conditions. A mechanism-critical control is also reported: replacing the analytic path with same-dimensional non-mechanistic features consistently degrades cross-condition performance, indicating that the analytic anchor is not interchangeable with generic auxiliary branches. Experiments on CWRU, SEU, and an agricultural-machinery-relevant test-rig dataset show in-domain accuracies of 99.48%, 98.50%, and 97.53%, respectively. In strict cross-speed evaluation on the test-rig dataset, the method achieves 98.22% accuracy when trained at 1500 r/min and tested at 2000 r/min, and 98.03% accuracy in the reverse setting. In cross-dataset evaluation using the shared normal, inner-race, outer-race, and rolling-element fault classes, the proposed method achieves the best average performance among the evaluated source-only baselines, including generic DG methods and recent bearing-diagnosis generalization methods. These comparisons include DPICEN and a protocol-matched single-source adaptation of FARNet, denoted FARNet-SS, both evaluated without target-domain access during training or model selection. The model remains lightweight, with 0.1348M parameters and 127.11M FLOPs for the neural forward pass.

Fault diagnosis of taper roller bearings needs to be accurate and efficient to ensure industrial machinery reliability. This research has developed a vibration-based fault diagnosis method which combines statistical feature ranking and machine learning classification. Vibration signals for the five different health conditions (healthy, inner race fault, outer race fault, roller fault, and cage fault) were collected from an SKF 32,206 taper roller bearing with changing speeds and loads. Two types of features, time-domain and frequency-domain, were derived and the features with the highest discriminating power were determined by one-way ANOVA and Kruskal Wallis statistical tests. Different combinations of features were tested with six classifiers: support vector machines (SVM), neural networks, discriminant analysis, naive bayes, decision trees, and nearest neighbor. It was found that Kruskal-Wallis feature-ranking benefits not only the result accuracy but also the computational efficiency, and the best feature set had 18 features. Thus, the linear SVM classifier yielded a classification accuracy of 99% and an AUC of 1, while requiring the least training time, demonstrating that it is suitable for real-time purposes. This work proposes a novel, dependable, and computationally efficient method of identifying faults in taper roller bearings, thus leading to greater automation of condition monitoring in industrial plants.

ABSTRACT The application of aqueous lubricants in geared transmissions offers enormous energy saving potential and can contribute towards climate neutrality. This potential is based primarily on the ultra-low friction within typical machine elements in geared transmissions. In elastohydrodynamically lubricated contacts, coefficients of friction lower than 0.01 have been measured in gears, which is referred to as superlubricity. Despite extensive research, this potential has not been transferred to industrial applications yet. This review summarizes the current state of knowledge on aqueous lubrication for geared transmissions. The primary focus is on aqueous polyalkylene glycols and aqueous glycerol as these base stocks have shown to be promising for the lubrication of machine elements such as gears, bearings and seals. The lubrication mechanisms as well as the friction reduction and energy saving potential, and the challenges arising by the application of aqueous lubricants with the specific machine elements of geared transmission are described. This includes gears, rolling element bearings, plain bearings, seals, and clutches. Challenges such as (tribo-)corrosion, material incompatibility, load-carrying capacity, and water evaporation are analyzed. The review concludes with a derivation of research gaps that need to be addressed to enable aqueous lubrication of geared transmissions.

This study investigates the onset of electrically induced damage in rolling element bearings (REBs). An electric discharge test rig (EDTR) was designed and developed to investigate the effects of load, speed, lubricant, and electrical power on bearing damage. In the EDTR, one race of a thrust ball bearing is used to control the motion of the balls, while the counter raceway was replaced by a polished bearing steel flat. This configuration allows for examination of the flat specimen for the extent and nature of damage, as well as re-polishing and subsequent use. The test bearings were comprised of a single steel ball and five ceramic balls to control the flow of electrical current. Key parameters, including lubricant film thickness, supply power, and applied thrust load, were systematically varied to evaluate their influence on the damage. The effect of electrical power was also investigated by applying both direct current (DC) and alternating current (AC). Surface profilometry was conducted along the running track to measure the damage morphology, followed by a statistical analysis of the pits created due to electric discharge. Surface analysis revealed two characteristic forms of electrically induced bearing damage (EIBD): discrete surface pitting along the running track and an indented surface profile in the central contact region, resulting from thermal softening and localized plastic deformation. The results show that electrical damage at the contact is a strong function of the lubricant film thickness. Thin films promote frequent discharge events and high pit density, whereas thick films limit discharge frequency but produce deeper pits when breakdown occurs. Increasing electrical power and load intensifies damage severity, increasing both pit density and surface indentation. Statistical analysis confirmed lubricant film thickness as the dominant factor governing EIBD, followed by electrical power.

The grease life of rolling bearings is strongly influenced by the lubricating film thickness, yet its transient evolution has not been extensively investigated. In this work, a mass balance framework is used to describe the competition between oil supply from grease bleed and oil loss at elastohydrodynamic lubrication (EHL) contacts, enabling the assessment of the characteristics of film thickness evolution over time. The analysis reveals an initial stabilization toward a characteristic film thickness plateau, resulting from a nearly constant early-stage oil supply and self-regulating nonlinear EHL losses. At longer times, the film thickness gradually decreases, governed by the characteristic time scale of grease bleed, indicating that oil availability and rate of supply control the long-term film behavior. The proposed framework provides a systematic basis for understanding film evolution in grease-lubricated cylindrical roller bearings and can be extended to incorporate more detailed physical mechanisms for accurate predictions.

To address the difficulty in identifying weak early fault impacts and fault characteristic frequencies of rolling bearings in vertical cattle-manure briquetting machines under strong background noise caused by low-speed, heavy-load operation and material compaction, this study proposes a denoising and fault feature extraction method based on the adaptive fusion of CEEMDAN, improved wavelet threshold denoising, and DBO. By constructing a kurtosis-permutation entropy composite evaluation index to select sensitive IMF components and using the DBO algorithm to adaptively optimize the adjustment factors in the improved threshold function, the proposed method effectively enhances bearing fault impulses under strong-noise conditions. Simulation results showed that, at an input signal-to-noise ratio of approximately - 15dB, the proposed method increased the signal-to-noise ratio to - 1.400dB and reduced the mean square error to 0.002152, outperforming single CEEMDAN and conventional wavelet threshold denoising. In measured bearing signals from a vertical cattle-manure briquetting machine with rolling-element spalling, the 8.5Hz fault characteristic frequency and its harmonics were clearly extracted. The method provides an effective signal-processing approach for bearing condition monitoring and early fault diagnosis in agricultural and animal husbandry equipment.

Rolling bearings are among the most vulnerable components in various types of rotary machinery, and accurate fault detection and localization are essential. When a rolling bearing fails, the signal is non-stationary, and the energy distribution of the vibration signal varies depending on the fault location. In traditional k-nearest neighbor (KNN) fault diagnosis algorithms, Euclidean distance is primarily used to measure the distance between sample points, which is not effective at capturing similarity across different spatial distributions. Moreover, these algorithms assume equal feature importance, which does not reflect the actual characteristics of fault vibration signals. This study proposes a KNN-based rolling bearing fault diagnosis method that incorporates distribution discrepancy and differential feature importance. First, vibration signals are decomposed using three-level wavelet packet decomposition, and the energy of each node at the third level is used as the fault feature. Then, the mean impact value (MIV) algorithm is used to determine the relative importance of each feature, and the Earth mover's distance (EMD) is applied to measure differences between spatial distributions. By integrating Euclidean distance with MIV and EMD and applying the KNN majority voting rule, fault diagnosis is performed. The experimental results indicate that this method achieves a diagnostic accuracy of 99.43%, representing a 5.97% improvement compared to traditional KNN methods. The proposed method demonstrates accurate and effective fault diagnosis performance on the rolling bearing datasets used in this study.

ABSTRACT Grease-lubrication degradation in rolling bearings can develop before localised raceway or rolling-element damage becomes evident, but the associated vibration response is weak, broadband, and often masked by speed- and load-related variations in vibration energy. This study proposes a Time-Frequency Granular Information Divergence Index (TF-GIDI) for unsupervised early monitoring and relubrication-effect verification across operating conditions. TF-GIDI aligns time-frequency representations using condition-specific healthy reference statistics, discretizes them into state fields, and quantifies patch-level distributional shifts relative to a healthy template using Jensen-Shannon divergence. The health indicator provides a scalar degradation trajectory and contribution heatmaps for identifying degradation-sensitive time-frequency regions. In a custom run-to-failure experiment, TF-GIDI detects the start degradation time (SDT) at 540 min, whereas the earliest alarm among eight baseline indicators occurs at 1345 min, yielding an 805 min earlier warning. During relubrication testing, TF-GIDI captures the degradation-recovery-renewed degradation trajectory and quantifies relubrication effectiveness. On public run-to-failure benchmarks, it achieves the lowest remaining useful life (RUL) prediction error. In the time-varying Paderborn LDM case, it reduces mean absolute error (MAE) by 22.8% relative to the second-best baseline indicator. Overall, TF-GIDI supports quantitative non-destructive evaluation (QNDE) by enabling early warning, interpretable degradation localisation, and relubrication-effect verification for grease-lubricated bearings.

Adaptive distribution transformation for enhanced bearing fault detection in independent cart systems.