Gearbox Bearings Research Articles

Diagnosis of incipient faults in wind turbine bearings based on ICEEMDAN–IMCKD

AbstractTo address the difficulty in extracting early fault feature signals of rolling bearings, this paper proposes a novel weak fault diagnosis method for rolling bearings. This method combines the Improved Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and the Improved Maximum Correlated Kurtosis Deconvolution (IMCKD). Utilizing the kurtosis criterion, the intrinsic mode functions obtained through ICEEMDAN are reconstructed and denoised using IMCKD, which significantly reduces noise in the measured signal. This approach maximizes the energy amplitude at the fault characteristic frequency, facilitating fault feature identification. Experimental studies on two test benches demonstrate that this method effectively reduces noise interference and highlights the fault frequency components. Compared with traditional methods, it significantly improves the signal‐to‐noise ratio and more accurately identifies fault features, meeting the requirements for discriminating rolling bearing faults. The method proposed in this study was applied to the measured vibration signals of the gearbox bearings in the new high‐speed wire department of a Long Products Mill. It successfully extracted weak characteristic information of early bearing faults, achieving the expected diagnostic results. This further validates the effectiveness of the ICEEMDAN–IMCKD method in practical engineering applications, demonstrating significant engineering value for detecting and extracting weak impact characteristics in rolling bearings.

Temperature rise of high-speed bearing in gearbox of 5 MW wind turbine based on Bayesian-LightGBM and improved PSO-SVM troubleshooting

5MW wind turbine gearbox high-speed bearing temperature rise failure is one of the important factors affecting the stable operation of the wind turbine, accurate prediction and timely diagnosis can effectively improve the efficiency of the wind turbine. In this paper, a combined modelling wind turbine gearbox high-speed bearing temperature rise prediction method based on Bayesian-LightGBM and improved PSO-SVM is proposed with a 5 MW wind turbine as the research object. Firstly, the initial dimensionality reduction of SCADA data is performed by sparse random projection matrix, which reduces the redundant data. Secondly, feature selection is performed on the remaining data using Bayesian-LightGBM to identify 13 key input feature parameters. Then, the hyperparameters of the PSO algorithm are optimised using Bayesian algorithm and further, the optimised PSO algorithm is applied to identify the SVM parameters. Finally, a simulation experiment platform is established based on MATLAB to verify the temperature rise of high-speed bearings in gearboxes of wind turbines by example calculation and comparative analysis. The results show that the model established in this paper is effective, the prediction results are accurate and the performance is stable, and then compared with the algorithms such as PSO-SVM, SVM, BP, etc, the coefficient of determination of the algorithm is greater than 0.994 in both the training set and the test set, and the average decision percentage error is around 1.88% in both.

Research on the service life of bearings in the gearbox of rolling mill transmission system under non-steady lubrication state

A rolling bearing plays a key role in the gearbox of the cold rolling mill transmission system. As a result, the degradation and failure of bearings can lead to unplanned shutdowns of the entire rolling mill system. Since on-site cold rolling mills work usually in non-steady lubrication rolling conditions originating form variations of multiple parameters, the uncertainty affecting bearing performance in a cold rolling mill transmission system increases, making it more difficult to assess health status and predict the remaining service life of bearings, Therefore, by establishing a coupling model describing the relationship for the rolling mill and normal /faulty gearboxes under non steady rolling conditions, quantitatively studies the influence of various rolling parameters of a rolling mill on the fatigue service life of bearings in the gearbox of the rolling mill transmission system, and verify the effectiveness of linear cumulative damage theory for bearing fatigue service life through experiments and on-site bearing vibration data. The results indicate that as the thickness of the strip steel inlet, lubricant viscosity, and rolling speed increase, or the thickness of the strip steel outlet and rolling roll radius decrease, the service life of bearings gradually decreases. As the fluctuation amplitude of various rolling parameters in the cold rolling mill increases, the service life of bearings in the gearbox of the rolling mill transmission system gradually shortens. Moreover, when the rolling parameter value or fluctuation amplitude increases to a specific values, the remaining service life of bearings will sharply decrease. Under the same rolling conditions, the service life of bearings in gearboxes with faults decreases more significantly than that in normal gearboxes.

Optimization of high-speed angular contact ball bearing for aircraft gearbox utilizing an evolutionary multi-objective algorithm, NSGA-III

We develop a program for predicting bearing performance, including the maximum contact stress, bearing power loss, minimum lubricating film thickness, static safety factor, and dynamic life, and an optimal design for high-speed angular contact ball bearings in aircraft gearboxes using optimization algorithms. A program is developed to predict bearing performance by calculating the loads and displacements of individual balls based on the bearing’s kinematic and static equilibrium equations. Next, an optimization program is developed with non-dominated sorting genetic algorithm III (NSGA-III), wherein calculated bearing performance indicators serve as constraints or objective functions. The performance of individuals within the final Pareto front is evaluated using the inter-criteria correlation (CRITIC) and weighted product methods. The optimization performances of NSGA-III and NSGA-II are compared based on their hypervolume indicators. A solution of the kinematic and static equilibrium equations under the load cases and duty cycles of real aircraft gearboxes is obtained, enhancing the prediction accuracy. Furthermore, optimization, including bearing performance metrics rarely considered in previous studies, is performed. Optimal specifications superior to reference bearings employed in aircraft gearboxes are obtained, enhancing all three objective functions or specific objective functions. The optimization performance of NSGA-III is confirmed to surpass that of conventional NSGA-II.

Advancing high-speed train gearbox durability: enhanced bearing load and contact stress through transition from helical to herringbone gears

High-speed trains typically utilize helical gear transmissions, which significantly impact the bearing load capacity and fatigue service performance of the gearbox bearings. This paper focuses on the gearbox bearings, establishing dynamic models for both helical gear and herringbone gear transmissions in high-speed trains. The modeling particularly emphasizes the precision of the bearings at the gearbox’s pinion and gear wheels. Using this model, a comparative analysis is conducted on the bearing loads and contact stresses of the gearbox bearings under uniform-speed operation between the two gear transmissions. The findings reveal that the helical gear transmission generates axial forces leading to severe load imbalance on the bearings at both sides of the large gear, and this imbalance intensifies with the increase in train speed. Consequently, this results in a significant increase in contact stress on the bearings on one side. The adoption of herringbone gear transmission effectively suppresses axial forces, resolving the load imbalance issue and substantially reducing the contact stress on the originally biased side of the bearings. The study demonstrates that employing herringbone gear transmission can significantly enhance the service performance of high-speed train gearbox bearings, thereby extending their service life.

Condition monitoring for planetary journal bearings in wind turbine gearboxes by means of acoustic measurements and machine learning

The use of journal bearings instead of rolling bearings as planetary bearings in wind turbine (WT) gearboxes is advantageous for the power density of the drive train. In addition, they can increase the reliability of the gearboxes as they can be operated wear-free in hydrodynamic operation, i.e. with fluid friction. The dynamic loading conditions in wind turbines, as well as special conditions such as insufficient lubrication and particle contamination, can lead to mixed friction operation and consequently to wear in the journal bearings. If mixed friction is not detected, damage and failure of the journal bearing and consequently of the WT gearbox may occur. Therefore, it is required to develop a Condition Monitoring System (CMS) to detect mixed friction in the journal bearings during operation of the WT. Preliminary investigations have shown that various friction conditions can be detected by acoustic measurements in combination with machine learning classifiers. Current investigations on CMS methods for journal bearings using acoustic measurement methods are limited to component level applications. A condition monitoring methodology for wind turbine gearbox journal bearings does not currently exist. A major challenge for CMS development for journal bearings in WT gearboxes is the transfer of methods already proven at component level to gearbox applications. In preparation for CMS application at the gearbox level, this paper presents an approach for monitoring different friction conditions of journal bearings based on acoustic measurements at a component test rig. For the classification of the friction state, different machine learning (ML)-based approaches trained on the acquired acoustic measurement data are compared with respect to the achieved classification accuracy. Knowledge of the robustness of the classification method, e.g. with respect to the distance of the sensor to the bearing, provides the necessary basis for the use of the CMS at the gearbox level. The investigations are carried out under operating conditions typical for planetary bearings in wind turbines. Classification performance is evaluated using a validated elasto-hydrodynamic simulation model. The aim of the work is to develop a method that detects friction classes in the journal bearing based on structure-borne sound measurements. Here, simulation results are used to train the algorithms. Finally, the demonstrated method will be successfully applied to a test rig for wind turbine gearbox journal bearing. Based on the results, an ML approach will be selected for application in gearboxes.

Predicting subsurface inclusion initiated butterfly-wing cracking under rolling contact fatigue

Wind turbine (WT) gearbox bearings experience premature failures. Damage characterisation of failed bearings has shown that subsurface micro cracks and butterfly-wing cracks are associated with non-metallic inclusions in the bearing raceways. The existing studies are unable to predict crack propagation under rolling contact fatigue considering shakedown and ratchetting when the material around an inclusion experiences sufficiently high levels of stresses. In this study, a finite element (FE) damage model based on the Continuum Damage Mechanics (CDM) is developed, integrated with the modelling of plastic deformation and kinematic hardening when the material around inclusion is subjected to alternating tension and compression. Two damage types of manganese-sulphide inclusions are investigated, showing significant effects of inclusion boundary separation and internal cracking on the subsurface RCF crack evolution. The modelling results also show that higher surface traction, overloads and varied loading sequences, commonly experienced by WTs in operation, have significant effects on the increase of the subsurface butterfly-wing crack lengths, because of early crack initiation and accelerated crack propagation. The developed CDM FE model has shown its effectiveness in predicting the damage evolution to gain new insights of complex interactions of a number of critical factors that can lead to the premature failure of bearings.

Iterative Laplace of Gaussian filter and improved Teager energy operator for bearing fault detection in gearboxes

Abstract The presence of cyclic impulses is considered to be a significant indicator of bearing defects. The extraction of fault-related information from gearboxes, however, poses a significant challenge due to the complex working conditions in gear transmission systems. These conditions often lead to fault-related impulse features being masked by inevitable background noise and vibration components. To address this issue, a novel iterative Laplace of Gaussian (LoG) filtering technique is proposed for the fault diagnosis field of bearings in gearboxes. An objective function incorporating Kurtosis statistics is used to iteratively update LoG coefficients instead of relying on fixed coefficients as in the original version, making the iterative LoG filtering technique better suited for handling fault-induced impact signals. Furthermore, an improved Teager energy operator (TEO) is developed to reduce the susceptibility of the original TEO to noise. The improvement involves incorporating a multi-resolution symmetric difference sequence into the discrete expression of TEO. This symmetric difference acts as a translation-invariant, sliding-window filter that, when combined with an energy operator, enables both energy detection and frequency filtering. The robustness against noise and sensitivity to frequency can be further improved by adjusting the multi-resolution parameter. The results of the experiments confirm the effectiveness of the proposed approach in identifying weak bearing fault features for gearboxes. Besides, the proposed method is also compared with the original LoG filter-based method and some competing methods, demonstrating its superior performance.

Multiscale-simulation method for the wear behaviour of planetary journal bearings in wind turbine gearboxes

Wind energy is one of the most important renewable energy sources. To maintain competitiveness wind energy needs to be cost effective. Increasing the performance of wind turbines can help to reduce the total cost of wind energy. The power output can be increased by an increase in turbine rotor size since the power increases disproportionately with the rotor diameter. The increase in rotor size inherently comes with an increase in overall wind turbine size to cope with the increasing loads. Since the nacelle weight cannot be increased without limitation, e.g. due to the effect on the tower and costs, an increase in power density is necessary. The usage of journal bearings instead of rolling bearings for the application in planetary gearboxes in wind turbines is one way to increase the power density and reliability of the drivetrain. When designed and operated correctly, journal bearings have a longer service lifetime than rolling bearings. Wear simulations aid in the design of journal bearings. This work presents a simulation method for the wear prediction of planetary journal bearings in wind turbine gearboxes. The method is validated with measurements on a component test rig. The transfer to the wind turbine gearbox is shown by means of simulation.

Experimental Characterization of Common-Mode Voltages and Currents in Gearbox Bearings of a Doubly-Fed Induction Generator Wind Turbine on a Test Bench

Wind turbine (WT) drivetrain failures can lead to expensive repairs and downtime, significantly impacting the Levelized Costs of Electricity (LCOE). Gearbox failures are a major contributor to maintenance costs, with High-Speed-Shaft (HSS) bearings having the highest failure rate. Frequently occurring premature HSS bearing damage indicates design inadequacies. Currently, bearing design standards (DIN/ISO 281) are primarily based on fatigue. Other damage mechanisms like adhesion or damages due to electrical currents passing through the bearings are not considered. WT gearbox bearings are exposed to electrical currents, mainly caused by common-mode (CM) voltages. Bearing currents lead to surface damages and also may promote the formation of white etching areas (WEA) or cracks (WEC) and thus need to be investigated further. The CM voltage is an inherent feature of the power converter, which generates CM currents in the electrical machine. The resulting high voltages at the HSS bearings can lead to electrical discharge currents in the rolling contact, which damage the raceways of the bearing. This paper presents an experimental method for characterization of the currents and voltages that pass from the generator to the HSS of the gearbox in a multi-megawatt WT drivetrain on a test bench. CM currents and voltages measured at the converter are correlated with the shaft voltages at the HSS bearing. The results indicate, that the CM currents directly pass the HSS and its bearings into the gearbox housing. The CM currents show peaks indicating discharge events with over 100 A. Furthermore, at the discharge events, high-frequency oscillations with over 20 MHz are observed in CM current and bearing voltage.

Method for accelerated testing of wind turbine gearbox bearings based on frictional energy in the rolling contact

This paper investigates a method for a novel accelerated test procedure for rolling bearings in wind turbine gearboxes. Established test procedures e. g. highly accelerated lifetime tests (HALT) mainly reflect fatigue damage and not slip-induced damage patterns (e.g. smearing). A commonly used criterion to rate the severity of an operating point regarding slip-induced damage is the frictional energy. Frictional energy is introduced into the rolling contact as soon as there is a significant combination of simultaneously occurring slip and pressure. Up to now only critical thresholds for this criterion that must not be exceeded have been identified on small-scale component test rigs. However, the permissible amount and duration of overshoots of the critical threshold that lead to damage in the actual application are not understood yet. Therefore, the aim is to conduct tests on full-size test rigs in which frictional energy is applied until slip-induced damage occurs. In order to perform these tests in a reasonable time, it is essential to accelerate the test procedures. Thus, this paper introduces a method for accelerated test procedures based on frictional energy in the rolling contact. The requirement is that the same cumulated frictional energy as in field operation is applied in a shorter time on the test rig. A further requirement is that the frictional power in the accelerated test procedure never exceeds the maximum frictional power occurring in the field. This paper shows the theoretical background regarding frictional energy and the transfer to the test procedure.

Diagnosis of EV Gearbox Bearing Fault Using Deep Learning-Based Signal Processing

Diagnosis of EV Gearbox Bearing Fault Using Deep Learning-Based Signal Processing

Research on transfer learning diagnosis method for planetary gearbox bearings based on variational embedding composite multiscale diversity entropy and manifold dynamic distribution adaptation

To improve the generalisation ability of traditional machine learning fault diagnosis model, this paper proposed a planetary gearbox bearing diagnosis method based on the variational embedded composite multiscale diversity entropy (veCMED) and manifold dynamic distribution adaptation (MDDA). Firstly, fault features are extracted using variational embedded composite multiscale diversity entropy (veCMDE). Secondly, the extracted entropy fault features are combined with manifold dynamic distribution adaptation (MDDA) of the flow shape to map the entropy values of different distributions to the space of the same distribution. Thirdly, the entropy value after feature mapping is input into the KNN classifier for fault identification. The final experimental results show that the proposed veCMDE and MDDA based transfer learning method has the best generalisation ability compared to seven transfer learning methods.

A method based on DWRNet and MGAU for RUL prediction of bearing with few samples

Accurately predicting the remaining useful life (RUL) of a bearing is crucial in the prognostics and reliability management of machinery. Owing to the cost of wind power operation and maintenance and commercial barriers, collecting early failure samples of gearbox bearings is costly. Accordingly, the prediction of the RUL of bearings with few samples remains a challenging problem. To address this challenge, a two-stage method based on DWRNet and MGAU is proposed to predict the RUL of bearings with few samples. First, a bearing’s health indicator (HI) is constructed using a dynamic weighted residual network (DWRNet), which utilizes a dynamic weighted residual block to fully extract the fault feature of the bearing. Then, a meta-gated adaptive unit (MGAU) neural network is implemented to predict RUL of bearings with few samples via a gated adaptive unit and multi-task learning. Finally, the prediction ability of the proposed method is verified using a dataset of bearings.

Intelligent multi-fault identification and classification of defective bearings in gearbox

Bearing faults in gearbox systems pose critical challenges to industrial operations, needing advanced diagnostic techniques for timely and accurate identification. In this paper, we propose a new hybrid method for automated classification and identification of defective bearings in gearbox systems with identical rotating frequencies. The method successfully segmented the signals and captured specific frequency components for deeper analysis employing three distinct signal processing approaches, ensemble empirical mode decomposition EEMD, wavelet packet transform WPT, empirical wavelet transform EWT. By decomposing vibration signals into discrete frequency bands using WPT, relevant features were extracted from each sub-band in the time domain, enabling the capturing of distinct fault characteristics across various frequency ranges. This extensive set of features is then served as inputs for machine learning algorithm in order to identify and classify the defective bearing in the gearbox system. Random forest RF, decision tree DT, ensemble tree ET classifiers showcased a notable accuracy in classifying different fault types and their localizations. The new approach shows the high performance of the diagnostic gearbox with a minimum of accuracy (Min = 99.95 %) and higher stability (standard deviation = 0.1).

Calculation and Validation of Planet Gear Sliding Bearings for a Three-Stage Wind Turbine Gearbox

In recent years, the trend towards larger wind turbines and higher power densities has led to increasing demands on planet gear bearings. The use of sliding bearings instead of rolling bearings in planetary bearings makes it possible to increase the power density with lower component costs and higher reliability. Therefore, the use of planet gear sliding bearings in wind turbine gearboxes has become more common. However, the flexible structure and complex load conditions from the helical tooth meshes lead to highly complex elastic structure deformation that modifies the lubricant film thickness and pressure distribution and, thus, has to be considered in the calculation of the bearing’s load-carrying capacity. This paper introduces a highly time-efficient calculation procedure that is validated with pressure measurement data from a three-stage planetary gearbox for a multi-megawatt wind energy plant. The investigations focus on three main objectives: (i) analyses of experimental and predicted results for different load cases, (ii) validation of the results of planet gear sliding bearing code, and (iii) discussion on mandatory modeling depths for the different planet stages. Results indicate the necessity of further research in this field of applications, particularly for the third-stage bearings.

Defect Categorization of Ribbon Blender Worm Gearbox Worm Wheel and Bearing Based on Artificial Neural Network

There is a demand for worm gearboxes in diversified industrial fields that include machinery such as escalators, ribbon blenders, pulverisers, bowl mills, etc. because of their peculiar characteristics like torque and quick retardation. The most commonly occurring defects in a worm gear box are scratches that develop in the worm gear and in bearings. Early defect categorization is required to prevent a sudden breakdown that would decrease production. The defect is depicted in different cases, which include defects in the gear tooth and the outer and inner races of the bearing. In another case, the defect is considered in the gear tooth as well as the bearing. The severity is designated using the ANN. The experiments were performed under these conditions with a good worm gearbox to capture vibration response signatures. Using these values as an input to the ANN, the model is trained. Experimental results show that vibration amplitude increases with fault progression in the worm gearbox, and the trained ANN model effectively categorizes worm gearbox faults with an accuracy of 97.12%.

A surface study of failed planetary wind turbine gearbox Bearings to investigate the causes of the Bearing premature failure issue

The advanced software used in designing Wind Turbine Gearboxes (WTGs) does not solve the premature failure of the gearbox bearings, which still fail within 5%–20% of their design life by flaking. This issue increases the maintenance, downtime, and early replacement costs that limit the investment in the wind energy field. The majority of the previous research have focused on bearing subsurface investigation and the microstructural changes associated with the failure patterns. Conversely, surface investigation can elucidate significant information about the loading levels and contributors to the premature bearing failure. In this study, two bearings from a planetary stage of a failed multi-megawatt wind turbine gearbox underwent surface investigations and analyses. The analyses include indentations, hardness, roughness, and severe damage regions. The study shows that the contact loading stress exceeds the recommended and more than the compressive yield stresses of the bearing materials. The loading distribution on the bearing inner race during the gearbox operation is quite different from the theoretical loading. The transient loadings throughout the service reduce the Wind Turbine Gearbox Bearings (WTGBs) service life. Furthermore, the significant effects of skewing and slipping have been confirmed. Accordingly, the, lubricant filtration system and the design of the planetary stage are recommended to be improved to extend the fatigue life of the wind turbine gearbox bearings.

The influence of lubricants on white etching areas formation in DLC-coated bearing steel under severe boundary lubrication

White etching areas (WEAs) and white etching cracks (WECs) are the catastrophic premature failure modes associated with wind turbine gearbox bearings. Several drivers, including lubricant chemistry, influence the evolution of WEAs and WECs. This study investigated the performance of base oils with various lubricant chemistry against microstructural decay in DLC-coated AISI 52100 bearing steel. Tests were performed in sliding-dynamic loading conditions under severe boundary lubrication. The effect of base oils against WEAs formation in DLC-coated steel is explored by analysing the condition of oils, wear behaviour, and subsurface microstructural change. The study outcomes revealed that the selection of base oils plays a vital role against the WEAs formation in DLC-coated bearing steel subsurface.

Research of the temperature regime of the support bearing units of a car gearbox

The technology for automatic digital thermal diagnostics of mechanical transmission components is presented using the example of gearbox bearings. The diagnostic criterion is substantiated, the problem of indirect determination of the temperature in the friction zone is solved, taking into account the influence of adjacent heat sources. Thermal imaging observation of the gearbox operation confirmed the effectiveness of the proposed diagnostics. Keywords transmission, bearing, testability, automatic diagnostics, thermal diagnostics