Bearing Test Research Articles

Machine learning based parameter estimation for an adapted finite element model of a blade bearing test bench

Improving the reliability of blade bearings is essential for the safe operation of wind turbines. This challenge can be met with the help of virtual testing and digital-twin driven condition monitoring. For such approaches, a precise digital representation of the blade bearing and its test bench is an essential prerequisite. However, various factors prevent the capture of all parameters of the blade bearing and the associated test bench. Parameters such as bearing preload, rolling element and raceway dimensions, and bolt preload during assembly vary with each bearing and test bench setup. As these parameters cannot be measured directly, an alternative solution is required. This article presents a methodology to efficiently estimate non-measurable parameters of the test bench using a combination of model-based and data-driven approaches, improving the detailed and accurate virtual testing of blade bearings. It must be ensured to enable the fastest possible, most computationally efficient estimation of parameters during virtual testing or condition monitoring. The developed methodology is evaluated using the example of bolt preload on the test bench. By employing a random forest model and the strain gauge measurements attached to the blade bearing, the bolt preload parameters are estimated. The results demonstrate that the accuracy of the digital model of the blade bearing test bench is improved by up to 11 % in three out of four test bench setups. The great improvement in the accuracy of the digital model highlights the effectiveness of the proposed methodology in enhancing virtual blade bearing testing and digital-twin driven condition monitoring.

Study on Bearing Mechanism of Steel Screw Pile

In order to study the bearing mechanism of a steel screw pile (SSP), a 3D-FEM of “SSP-stratum” was established based on the large-scale general finite element analysis platform ABAQUS. Secondly, the real friction coefficient between pile and soil was determined by comparing it with the field bearing test data of screw piles. Finally, the bearing mechanism and failure criterion of the SSP was revealed. The research showed that the pile-soil friction coefficient was about 0.6 under the condition of this stratum, and the screw pile was in the elastic working stage under the conditions of compression and tension load, which had a large bearing reserve. The magnitude of Young’s modulus was inversely correlated with the settlement value and the extreme value of principal stress. The increase in the Young’s modulus of the stratum was helpful in improving the bearing capacity of the screw pile. The compressive capacity of circular steel tube + screw pile (CST + SP) was about 2 times higher than CST, and the compressive capacity of circular steel tube + large blade + screw pile (CST + LB + SP) was about 2.4 times higher than CST. The tensile capacity of CST + SP was about 3.4 times higher than CST, and the tensile capacity of CST + LB + SP was about 4.9 times higher than CST. The arrangement of large blades better bore the load of the screw part and optimized the stress distribution of the structure. Based on the mechanical analysis in the vertical direction, the bearing mechanism of the SSP under compression and tension conditions was elaborated. The bearing failure criterion of the SSP was summarized from the aspects of mechanics and displacement. The bearing design of the SSP should meet the control of mechanics and deformation at the same time. The research work could provide a useful reference for the design and construction of SSPs.

Fabrication of Cu-Infiltrated Journal Bearing by Binder Jetting Additive Manufacturing

In this study, considering the economic feasibility of products that can be produced through the binder jetting additive manufacturing process, 316L stainless steel, a widely used material with a wide particle size ranging from 15 to 106 μm, was used. The lubrication effect was increased by internal patterning through design for additive manufacturing, and journal bearing parts with excellent load resistance and wear resistance were implemented by using wear-resistant Cu as an infiltration material. In addition, to investigate the amount of Cu infiltrated as a function of porosity, the parts were pre-sintered from 1423 K to 1573 K, and the best performance was obtained when Cu was infiltrated after pre-sintering at 1473 K. As a result of rig testing of Cu-infiltrated journal bearings, mechanical properties were obtained that were more than 50% improved compared to those of mass products.

Investigation of the Discharge Voltage in Electric Vehicle Motor Bearings

As the automotive industry undergoes a significant transition towards electric vehicles (EV), the matter of electric current discharge in motor bearings has emerged as a focal point of concern. This issue has gathered increased attention due to its potential to inflict damage upon bearing surfaces. The extent of damage inflicted by the discharges is intricately tied to the discharge energy, a factor directly influenced by both bearing capacitance and discharge voltage. In pursuit of a deeper understanding of discharge voltage dynamics within EV motor bearings, electric discharge tests were conducted using in-house modified single ball-on-disc contact and full bearing test equipment. Our test results at the single ball-on-disc contact show that discharge voltage adheres to a probabilistic distribution. Moreover, parallel investigations at the bearing level have yielded corroborative findings, reinforcing conclusions drawn from the single contact tests. These collective efforts contribute to a comprehensive understanding of electric discharge phenomena in motor bearings, essential for developing effective mitigation strategies and advancing the reliability of EV propulsion systems.

Impact of Crude Oil Pollution on some Geotechnical Characteristics of North Rumaila Soils in the Southern Iraqi Province of Basrah Governorate

Background: Pollution affects the soil and results in a change in its natural, chemical, or organic characteristics and properties, which directly or indirectly affect the geotechnical properties of the soil and make it unsuitable for engineering use. The aim of the study is to determine how adding various amounts of crude oil to the study area's soil affects the soil's geotechnical characteristics, including soil shear resistance, compaction coefficients, and California bearing capacity. Materials and Methods: The North Rumaila site was chosen for the study. At a depth of one meter, natural, pollution-free soil was extracted, and weight percentages of 2%, 4%, 6%, 8%, and 10% of crude oil were added. Grain size analysis, moisture content, direct shear, compaction, and Californian bearing tests were conducted. Results: The findings indicate that the soil at the North Rumaila site has a moisture content of 6.45% and is mostly silty sand with small amounts of clay and gravel. Crude oil was added to the soil, which caused the Californian to lose some of its bearing capacity, the optimal moisture content to raise, the angle of internal friction to decrease, and the bearing capacity of the soil to decrease. Conclusion: Pollution by crude oil effects negatively impacted the soil's geotechnical qualities.

Multi-Parametric Investigations on White Etching Crack Formation in Deep Grove Ball Bearings

Research on White Etching Cracks (WEC) in multiple bearing applications has identified various drivers that cause them. Lubricants and electricity combined with contact mechanics have been proven to catalyze WEC significantly. However, none of these factors solely cause WEC on its own; instead, combinations of factors discretize whether WEC appears or not. Hence, the WEC phenomenon appears to be multidimensional, making WEC still unpredictable. The current paper is about a systematic study using a Deep Grove Ball Bearing test rig to investigate how lubricant chemicals, combined with electricity and variations in oil flow and pressure, lead to WEC formation. It becomes evident that even under critical conditions for WEC formation, increasing oil flow and decreasing contact pressure can prevent WEC.

Full-scale experimental study on tribological performance of FPBs for highway bridges with various solid lubricants under fast loading

Friction pendulum bearings (FPBs) have proven to be effective for the seismic isolation of highway bridges. Previous studies often employed scaled model shaking table tests to investigate dynamic responses of the structures or quasi-static tests to assess the hysteretic performance of FPBs. However, it is noteworthy that the stress on the friction surface is reduced due to the size effect in scaled model in shaking table tests, while quasi-static tests of prototype bearings cannot simulate the sliding velocity experienced during seismic events, which is a sensitive parameter for the performance of FPBs with solid lubricant plates. Research on the performance of full-scale FPBs under fast frictional loading is very limited. This paper addresses this knowledge gap by studying the velocity demand for a highway bridge with FPBs and conducting fast sliding performance tests using full-scale FPBs. The results indicate that under a 0.2 g PGA earthquake, the velocity demand for FPBs can reach 0.5 m/s. Through testing full-scale FPBs at various loading speeds, it was found that solid lubricant plates by UHMWPE material exhibit poor thermal stability during wear tests, solid lubricant plates by PTFE material lack sufficient tangential tear strength under fast loading, and solid lubricant plates by PET material show good wear resistance but have a high breakaway force and levered maximum force during the running-in stage. Subsequently, this study developed ultra-high performance friction plate (UHPF) as the solid lubricant for FPBs, achieving ideal hysteretic performance under fast loading. The full-scale FPB specimens in this paper are the ones manufactured for the continuous beam bridges in the Shenzhen-Zhongshan Sea-crossing Link project in China. The dimensions of the bearing, axial load, and loading speed is much more stringent than those in previous FPB tests, providing results that are highly important for engineering applications.

EXPERIMENTAL TESTS OF RED MERANTI (SHOREA SPP.) DOWEL BEARING STRENGTH AT AN ANGLE TO THE GRAIN

The angle to the grain has a significant influence on timber bearing strength. As the grain angle increases the bearing strength decreases. The aim of this research was to obtain the dowel bearing strength of the red meranti (Shorea spp.) timber at an angle to the grain. The scope of this research was as follows the specimens were made according to ASTM D143, the grain angle ranged from 0° to 12°, and the dowel bearing tests were displacement controlled in accordance with ASTM D5764. Results of this research was an empirical equation of dowel bearing strength (in MPa) in terms of an angle to grain θ (in degrees) namely Fe = 32.74 - 4.701θ + 0.2064θ2. The importance of studying the influence of the grain angle to the dowel bearing strength for timber connection design is because the direction of the timber grain is not perfectly 0°.

Premature Damage in Bearing Steel in Relation with Residual Stresses and Hydrogen Trapping

In this study, premature damage in cylindrical roller bearings made of 100Cr6 (SAE 52100) was investigated. For this purpose, full bearing tests were carried out using two different lubricant formulations with similar viscosities. Published research has pointed out the occurrence of tribo-chemical reactions that cause lubricant degradation and the release of hydrogen in tribo-contact. Hydrogen content measurements were conducted on tested samples, and these measurements showed dependence on the lubricant formulations. Hydrogen diffusion and trapping were identified as significant factors influencing premature damage. The measurement of trapping energies was conducted by thermal desorption spectroscopy, whereas residual stresses, which influence hydrogen diffusion and accumulation, were measured using X-ray diffraction. The measured trapping energies indicated that rolling contact caused the creation and release of hydrogen traps. Over-rolling resulted in changes in residual stress profiles in the materials, demonstrated by changes in stress gradients. These can be directly linked to subsurface hydrogen accumulation. Hence, it was possible to determine that the location of the microstructural damage (WEC) was correlated with the residual stress profiles and the subsurface von Mises stress peaks.

Incorporating bearing flexibility into a digital twin for supervised machine learning-based ball bearing diagnostics

Vibration analysis remains the most used technique for the maintenance of rotating machines, despite requiring their availability. However, with the emergence of Industry 4.0, digital twins provide the opportunity to generate signals of several operating modes. These signals can be used to diagnose or predict the state of the machine being monitored. The digital twins rely on working on hypotheses in the conception of the numerical model. Rolling bearings are one of the most critical components in rotating machines, due to their high requirements for machine dynamics. In this study, the flexibility of the bearings are integrated into the numerical model of a rolling bearings test bench. To achieve this, a discrete element model and a finite element model is created, which communicate with each other to translate the structure’s dynamics. To convert the numerical model into a digital, it is recalibrated on the basis of the signals measured on the test bench, by minimizing an objective function. Then, to assess the reliability of the data generated by the digital twin, a diagnostic by classification is performed. The data generated by the digital twin are used to train a MSVM classification algorithm. Predictions are made on the experimental data initially acquired, describing the same operating modes as the generated data. The results show an improvement in the classification accuracy with the model integrating flexibility compared to the model existing in the literature.

A novel RUL prediction method for rolling bearings based on dynamic control chart and adaptive incremental filtering

Abstract Degradation of rolling bearings typically consists of two stages: a stable stage (Stage I) characterized by stable fluctuations in the health indicator (HI), and a degradation stage (Stage II) where early damage leads to HI degradation, eventually reaching the failure threshold. Therefore, to achieve online RUL prediction for bearings, three aspects should be studied: 1) Degradation modeling; 2) Inter stage change point identification; 3) Online degradation state updating. Firstly, a two-stage degradation model is constructed by simultaneously considering inherent randomness, individual differences, and measurement errors. Then, a dynamic statistical process control (SPC) method is proposed to identify the change point from Stage I to Stage II. The SPC is designed to dynamically control limits based on the bearing's condition monitoring (CM) data to prevent false alarms. An adaptive incremental filtering (AIF) is proposed to update the degradation states by simultaneously considering the state increment and the dynamics of the system noise and measurement noise. The effectiveness of the proposed method is validated on 16004 bearing test data and XJTU-SY bearing data. Results show that the proposed method can accuracy identify the change point and improve the accuracy of the prediction result during stage II.

Remaining useful life prediction of slewing bearings using attention mechanism enabled multivariable gated recurrent unit network

It is difficult to obtain the damage information on large slewing bearings only from vibration signals. In addition, deep learning models trained on old samples do not achieve high accuracy in new tasks. Therefore, this paper uses vibration, temperature, and torque signals of slewing bearings to build a model. Meanwhile, we add attention mechanism to capture internal correlation of them to consider the related factors of remaining useful life (RUL) from multiple angles. The multivariable gated recurrent unit (GRU) based on attention mechanism gated recurrent unit (attention-MGRU) model is adopted to improve the prediction performance. On this foundation, a fine-tuning strategy is introduced to improve the generalization ability of the model. A full-life accelerated test is carried out on the slewing bearing test bench. The model proposed in this paper is compared with GRU prediction model, which utilizes vibration signals and multivariable GRU prediction model. Mean absolute error (MAE) and root-mean-square error (RMSE) are used as measurement indicators. Among different methods, three indicators generated by attention-MGRU show significant superiority. Moreover, the fine-tuned model performs better in new tasks compared with the original model.

A New Strategy: Remaining Useful Life Prediction of Wind Power Bearings Based on Deep Learning under Data Missing Conditions

With its formidable nonlinear mapping capabilities, deep learning has been widely applied in bearing remaining useful life (RUL) prediction. Given that equipment in actual work is subject to numerous disturbances, the collected data tends to exhibit random missing values. Furthermore, due to the dynamic nature of wind turbine environments, LSTM models relying on manually set parameters exhibit certain limitations. Considering these factors can lead to issues with the accuracy of predictive models when forecasting the remaining useful life (RUL) of wind turbine bearings. In light of this issue, a novel strategy for predicting the remaining life of wind turbine bearings under data scarcity conditions is proposed. Firstly, the average similarity (AS) is introduced to reconstruct the discriminator of the Generative Adversarial Imputation Nets (GAIN), and the adversarial process between the generative module and the discriminant is strengthened. Based on this, the dung beetle algorithm (DBO) is used to optimize multiple parameters of the long-term and short-term memory network (LSTM), and the complete data after filling is used as the input data of the optimized LSTM to realize the prediction of the remaining life of the wind power bearing. The effectiveness of the proposed method is verified by the full-life data test of bearings. The results show that, under the condition of missing data, the new strategy of AS-GAIN-LSTM is used to predict the RUL of wind turbine bearings, which has a more stable prediction performance.

Study on bearing capacity of corroded pipes before and after spraying rehabilitation

In recent years, there has been growing interest in trenchless technology, particularly in using centrifugal spraying technology to rehabilitate drainage pipelines. In this study, we evaluated the effectiveness of fiber-reinforced cement mortar spraying materials on pipes using the three-edge bearing test (TEBT) as per ASTM C497 standards. Six conditions of corroded and rehabilitated pipelines were tested: (1) corrosion depths, (2) corrosion widths, (3) corrosion positions, (4) pipe diameters, (5) loading rates, and (6) repair thicknesses. Through the TEBT, the structural bearing capacity and failure form of the pipe before and after repair were compared, and the bearing capacity difference of the pipe under different conditions was studied. Finally, a sensitivity analysis was conducted on the six variables. Among the many factors that affect the reliability of pipe structures, the influence degree of each factor is quite different. Through the parameter sensitivity analysis, the influence degree of each factor on the reliability of the structure can be clarified. The results show that the bearing capacity of the rehabilitated pipes is sufficient to meet the normal pipe bearing capacity requirements. The mortar lining and existing pipes can bear stress jointly, significantly increasing the maximum load of the rehabilitated pipes by up to 55.6 % and correspondingly reducing the circumferential strain. This indicates that the application of the mortar spraying lining layer effectively achieves the purpose of pipeline repair. This study provides valuable guidance for structural design optimization and safe operation.

Adaptive Low-Rank Tensor Estimation Model Based Multichannel Weak Fault Detection for Bearings.

Multichannel signals contain an abundance of fault characteristic information on equipment and show greater potential for weak fault characteristics extraction and early fault detection. However, how to effectively utilize the advantages of multichannel signals with their information richness while eliminating interference components caused by strong background noise and information redundancy to achieve accurate extraction of fault characteristics is still challenging for mechanical fault diagnosis based on multichannel signals. To address this issue, an effective weak fault detection framework for multichannel signals is proposed in this paper. Firstly, the advantages of a tensor on characterizing fault information were displayed, and the low-rank property of multichannel fault signals in a tensor domain is revealed through tensor singular value decomposition. Secondly, to tackle weak fault characteristics extraction from multichannel signals under strong background noise, an adaptive threshold function is introduced, and an adaptive low-rank tensor estimation model is constructed. Thirdly, to further improve the accurate estimation of weak fault characteristics from multichannel signals, a new sparsity metric-oriented parameter optimization strategy is provided for the adaptive low-rank tensor estimation model. Finally, an effective multichannel weak fault detection framework is formed for rolling bearings. Multichannel data from the repeatable simulation, the publicly available XJTU-SY whole lifetime datasets and an accelerated fatigue test of rolling bearings are used to validate the effectiveness and practicality of the proposed method. Excellent results are obtained in multichannel weak fault detection with strong background noise, especially for early fault detection.

Results of wear endurance runs of wind turbine pitch bearings

Pitch bearings of wind turbines connect the rotor blade and the rotor hub. They have to cope with highly dynamic and stochastic loads and oscillating movements for lifetimes of 20 years and more. While several test rigs for pitch bearings are in operation, only very limited data is publicly accessible. Oscillating bearings typically suffer from wear, and this work covers the results of wear endurance tests of full-scale pitch bearings. Four bearings were tested approximately 120 days each under realistic conditions which included dynamic loads in five degrees of freedom. The bearings did not show any or very limited signs of raceway wear, in contrast to results of small-scale tests and short-term tests under worst-case conditions.

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.

An Integrated Dimension Theory and Modulation Signal Bispectrum Technique for Analyzing Bearing Fault in Industrial Fibrizer

Abstract This study investigates the dynamics of roller bearings by utilizing the dimension theory technique to diagnose the bearing clearance faults of revolving machines. The generation of local defects in rotating machines is closely related to the clearance behaviour of the rotor-bearing system. A dynamic model of bearing with dimension theory by matrix method (DTMM) is developed for characteristics of bearing clearance considering the influence of local defects on the inner and outer bearings races. The characteristics of bearing internal radial clearance considering the impact of the defect on the bearing are analyzed. An experimental study has been performed under various operational conditions. The noisy signal is subsequently eliminated using the Modulation Signal Bispectrum (MSB). The efficiency and reliability of the stated approach are evaluated using a specialized bearing test and a run-to-failure fibrizer test. As a result, this technology offers a significant opportunity to execute more cost-effective maintenance work to eliminate the breakdown of machinery.

Enhanced Bearing Fault Diagnosis Through Trees Ensemble Method and Feature Importance Analysis

Abstract Purpose This research introduces a groundbreaking method for bearing defect detection. It leverages ensemble machine learning (ML) models and conducts comprehensive feature importance analysis. The key innovation is the training and benchmarking of three tree ensemble models—Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—on an extensive experimental dataset (QU-DMBF) collected from bearing tests with seeded defects of varying sizes on the inner and outer raceways under different operating conditions. Method The dataset was meticulously prepared with categorical variable encoding and Min–Max data normalization to ensure consistent class distribution and model accuracy. Implementing the ML models involved a grid search method for hyperparameter tuning, focusing on reporting the models’ accuracy. The study also explores applying ensemble methods and using supervised and unsupervised learning algorithms for bearing fault detection. It underscores the value of feature importance analysis in understanding the contributions of specific inputs to the model’s performance. The research compares the ML models to traditional methods and discusses their potential for advanced fault diagnosis in bearing systems. Results and Conclusions The XGBoost model, trained on data from actual bearing tests, outperformed the others, achieving 92% accuracy in detecting bearing health and fault location. However, a deeper analysis of feature importance reveals that the models weigh certain experimental conditions differently—such as sensor location and motor speed. This research’s primary novelties and contributions are comparative evaluation, experimental validation, accuracy benchmarking, and interpretable feature importance analysis. This comprehensive methodology advances the bearing health monitoring field and has significant practical implications for condition-based maintenance, potentially leading to substantial cost savings and improved operational efficiency.

Hydrodynamic Thrust Bearing Test Rig with Novel Bearing Alignment System

Abstract This study presents the design and development of a new hydrodynamic thrust bearing test rig with a novel (patent pending) hydrodynamic pressure feedback bearing alignment control system. The bearing alignment system maintains even distribution of thrust load across the bearing thrust pads using active hydrodynamic pressure as input to manipulate bearing orientation with stepper motors. The test rig is used to conduct performance evaluation of fixed geometry hydrodynamic thrust bearings with variable taper depths. The influence of helical taper angle, rotational speed, and applied load on key performance characteristics including minimum oil film thickness (MOFT), hydrodynamic pressure distribution, and bearing temperature is presented and analyzed. A numerical model based on the Reynolds equation is used to support the experimental results obtained. Trends in performance established by the numerical analysis show mutually agreeable results compared to experimental data. The average percent deviation of the experimentally gathered change in MOFT as load is increased with respect to the numerically predicted values is 24%. Comparison of experimental to numerical pressure distribution data shows an overall average percent deviation of 32%.