Design and optimization of fluid lubricated bearings operated with extreme working performances——a comprehensive review

Fluid lubricated bearings have been widely adopted as support components for high-end equipment in metrology,semiconductor devices,aviation,strategic defense,ultraprecision manufacturing,medical treatment,and power generation.In all these applications,the equipment must deliver extreme working performances such as ultraprecise movement,ultrahigh rotation speed,ultraheavy bearing loads,ultrahigh environmental temperatures,strong radiation resistance,and high vacuum operation,which have challenged the design and optimization of reliable fluid lubricated bearings.Breakthrough of any related bottlenecks will promote the development course of high-end equipment.To promote the advancement of high-end equipment,this paper reviews the design and optimization of fluid lubricated bearings operated at typical extreme working performances,targeting the realization of extreme working performances,current challenges and solutions,underlying deficiencies,and promising developmental directions.This paper can guide the selection of suitable fluid lubricated bearings and optimize their structures to meet their required working performances.

A Hybrid Approach for Predicting the Remaining Useful Life of Bearings Based on the RReliefF Algorithm and Extreme Learning Machine

Accurately predicting the remaining useful life(RUL)of bearings in mining rotating equipment is vital for mining enterprises.This research aims to distinguish the features associated with the RUL of bearings and propose a prediction model based on these selected features.This study proposes a hybrid predictive model to assess the RUL of rolling element bearings.The proposed model begins with the pre-processing of bearing vibration signals to reconstruct sixty time-domain features.The hybrid model selects relevant features from the sixty time-domain features of the vibration signal by adopting the RReliefF feature selection algorithm.Subsequently,the extreme learning machine(ELM)approach is applied to develop a predictive model of RUL based on the optimal features.The model is trained by optimizing its parameters via the grid search approach.The training datasets are adjusted to make them most suitable for the regression model using the cross-validation method.The proposed hybrid model is analyzed and validated using the vibration data taken from the public XJTU-SY rolling element-bearing database.The comparison is constructed with other traditional models.The experimental test results demonstrated that the proposed approach can predict the RUL of bearings with a reliable degree of accuracy.

Intelligent Fault Diagnosis Method of Rolling Bearings Based on Transfer Residual Swin Transformer with Shifted Windows

Due to their robust learning and expression ability for complex features,the deep learning(DL)model plays a vital role in bearing fault diagnosis.However,since there are fewer labeled samples in fault diagnosis,the depth of DL models in fault diagnosis is generally shallower than that of DL models in other fields,which limits the diagnostic performance.To solve this problem,a novel transfer residual Swin Transformer(RST)is proposed for rolling bearings in this paper.RST has 24 residual self-attention layers,which use the hierarchical design and the shifted window-based residual self-attention.Combined with transfer learning techniques,the transfer RST model uses pre-trained parameters from ImageNet.A new end-to-end method for fault diagnosis based on deep transfer RST is proposed.Firstly,wavelet transform transforms the vibration signal into a wavelet time-frequency diagram.The signal’s time-frequency domain representation can be represented simultaneously.Secondly,the wavelet time-frequency diagram is the input of the RST model to obtain the fault type.Finally,our method is verified on public and self-built datasets.Experimental results show the superior performance of our method by comparing it with a shallow neural network.

Vibration Suppression for Active Magnetic Bearings Using Adaptive Filter with Iterative Search Algorithm

Active Magnetic Bearing(AMB) is a kind of electromagnetic support that makes the rotor movement frictionless and can suppress rotor vibration by controlling the magnetic force. The most common approach to restrain the rotor vibration in AMBs is to adopt a notch filter or adaptive filter in the AMB controller. However, these methods cannot obtain the precise amplitude and phase of the compensation current. Thus, they are not so effective in terms of suppressing the vibrations of the fundamental and other harmonic orders over the whole speed range. To improve the vibration suppression performance of AMBs,an adaptive filter based on Least Mean Square(LMS) is applied to extract the vibration signals from the rotor displacement signal. An Iterative Search Algorithm(ISA) is proposed in this paper to obtain the corresponding relationship between the compensation current and vibration signals. The ISA is responsible for searching the compensating amplitude and shifting phase online for the LMS filter, enabling the AMB controller to generate the corresponding compensation force for vibration suppression. The results of ISA are recorded to suppress vibration using the Look-Up Table(LUT) in variable speed range. Comprehensive simulations and experimental validations are carried out in fixed and variable speed range, and the results demonstrate that by employing the ISA, vibrations of the fundamental and other harmonic orders are suppressed effectively.

Research on the Influence of Raceway Waviness on the Vibration Characteristics of Deep Groove Ball Bearings

The dynamics model of a 2-degree-of-freedom deep groove ball bearing is established by incorporating the raceway surface waviness model comprising multiple sinusoidal functions superposition.The model is solved using the fourth-order Runge-Kutta method to obtain the vibration characteristics including displacement,velocity,acceleration,and frequency of the bearing.Validation of the model is accomplished through comparison with theoretical vibration frequencies.The influence of the amplitude of waviness of the inner and outer ring raceway surfaces of deep groove ball bearings on the vibration displacement,peak-to-peak vibration displacement and root-mean-square vibration acceleration is analyzed,and the results show that as the amplitude of the inner and outer ring raceway surfaces waviness increases,all the vibration characteristic indexes increase,indicating that the vibration amplitude of the bearings as well as the energy of the waviness-induced shock waveforms increase with the increase of the amplitude of the waviness.

Effect of Surface Roughness on Lubrication Performance of Bump-Type Gas Foil Bearings

Taking bump-type gas foil bearings as the research object,a deformation model of bump foil and a thin-plate finite element model of top foil were proposed.By solving Reynolds equation and energy equation,the pressure distribution and the temperature distribution of gas films in foil bearings were obtained.Further,a numerical method for calculating the lubrication performance of gas foil bearings with considering the surface roughness was proposed.With a specific example,effects of the surface roughness on the bearing lubrication performance were parametrically studied.The results indicate that rougher journal surface can lead to larger fluctuation of the lubrication performance,while surface roughness of top foil has few effects on the fluctuation.Moreover,the mean values of performance parameters almost remain constant at different values of surface roughness.

RMA-CNN:A Residual Mixed Domain Attention CNN for Bearings Fault Diagnosis and Its Time-Frequency Domain Interpretability

Early fault diagnosis of bearings is crucial for ensuring safe and reliable operations.Convolutional neural networks(CNNs)have achieved significant breakthroughs in machinery fault diagnosis.However,complex and varying working conditions can lead to inter-class similarity and intra-class variability in datasets,making it more challenging for CNNs to learn discriminative features.Furthermore,CNNs are often considered“black boxes”and lack sufficient interpretability in the fault diagnosis field.To address these issues,this paper introduces a residual mixed domain attention CNN method,referred to as RMA-CNN.This method comprises multiple residual mixed domain attention modules(RMAMs),each employing one attention mechanism to emphasize meaningful features in both time and channel domains.This significantly enhances the network’s ability to learn fault-related features.Moreover,we conduct an in-depth analysis of the inherent feature learning mechanism of the attention module RMAM to improve the interpretability of CNNs in fault diagnosis applications.Experiments conducted on two datasets—a high-speed aeronautical bearing dataset and a motor bearing dataset—demonstrate that the RMA-CNN achieves remarkable results in diagnostic tasks.

Analytical Modeling and Mechanism Analysis of Time-Varying Excitation for Surface Defects in Rolling Element Bearings

Surface defects,including dents,spalls,and cracks,for rolling element bearings are the most common faults in rotating machinery.The accurate model for the time-varying excitation is the basis for the vibration mechanism analysis and fault feature extraction.However,in conventional investigations,this issue is not well and fully addressed from the perspective of theoretical analysis and physical derivation.In this study,an improved analytical model for time-varying displacement excitations(TVDEs)caused by surface defects is theoretically formulated.First and foremost,the physical mechanism for the effect of defect sizes on the physical process of rolling element-defect interaction is revealed.According to the physical interaction mechanism between the rolling element and different types of defects,the relationship between time-varying displacement pulse and defect sizes is further analytically derived.With the obtained time-varying displacement pulse,the dynamic model for the deep groove bearings considering the internal excitation caused by the surface defect is established.The nonlinear vibration responses and fault features induced by surface defects are analyzed using the proposed TVDE model.The results suggest that the presence of surface defects may result in the occurrence of the dual-impulse phenomenon,which can serve as indexes for surface-defect fault diagnosis.

Deep Residual Joint Transfer Strategy for Cross-Condition Fault Diagnosis of Rolling Bearings

Rolling bearings are key components of the drivetrain in wind turbines,and their health is critical to wind turbine operation.In practical diagnosis tasks,the vibration signal is usually interspersed with many disturbing components,and the variation of operating conditions leads to unbalanced data distribution among different conditions.Although intelligent diagnosis methods based on deep learning have been intensively studied,it is still challenging to diagnose rolling bearing faults with small amounts of samples.To address the above issue,we introduce the deep residual joint transfer strategy method for the cross-condition fault diagnosis of rolling bearings.One-dimensional vibration signals are pre-processed by overlapping feature extraction techniques to fully extract fault characteristics.The deep residual network is trained in training tasks with sufficient samples,for fault pattern classification.Subsequently,three transfer strategies are used to explore the generalizability and adaptability of the pre-trained models to the data distribution in target tasks.Among them,the feature transferability between different tasks is explored by model transfer,and it is validated that minimizing data differences of tasks through a dual-stream adaptation structure helps to enhance generalization of the models to the target tasks.In the experiments of rolling bearing faults with unbalanced data conditions,localized faults of motor bearings and planet bearings are successfully identified,and good fault classification results are achieved,which provide guidance for the cross-condition fault diagnosis of rolling bearings with small amounts of training data.

Control System Design for Low PowerMagnetic Bearings in a Flywheel Energy Storage System

This paper presents a theoretical and experimental study on controller design for the AMBs in a small-scale flywheel energy storage system,where the main goals are to achieve low energy consumption and improved rotordynamic stability.A H-infinity optimal control synthesis procedure is defined for the permanent-magnet-biased AMB-rotor system with 4 degrees of freedom.Through the choice of design weighting functions,notch filter characteristics are incorporated within the controller to reduce AMB current components caused by rotor vibration at the synchronous frequency and higher harmonics.Experimental tests are used to validate the controller design methodology and provide comparative results on performance and efficiency.The results show that the H-infinity controller is able to achieve stable rotor levitation and reduce AMB power consumption by more than 40%(from 4.80 to 2.64 Watts)compared with the conventional PD control method.Additionally,the H-infinity controller can prevent vibrational instability of the rotor nutation mode,which is prone to occur when operating with high rotational speeds.

Optimal Design of Tapered Roller Bearings Based on Multi⁃Physics Objectives Using Evolutionary Algorithms

Rolling element bearing is the most common machine element in rotating machinery.An extended life is among the foremost imperative standards in the optimal design of rolling element bearings,which confide on the fatigue failure,wear,and thermal conditions of bearings.To fill the gap,in the current work,all three objectives of a tapered roller bearing have been innovatively considered respectively,which are the dynamic capacity,elasto-hydrodynamic lubrication(EHL)minimum film⁃thickness,and maximum bearing temperature.These objective function formulations are presented,associated design variables are identified,and constraints are discussed.To solve complex non⁃linear constrained optimization formulations,a best⁃practice design procedure was investigated using the Artificial Bee Colony(ABC)algorithms.A sensitivity analysis of several geometric design variables was conducted to observe the difference in all three objectives.An excellent enhancement was found in the bearing designs that have been optimized as compared with bearing standards and previously published works.The present study will definitely add to the present experience based design followed in bearing industries to save time and obtain assessment of bearing performance before manufacturing.To verify the improvement,an experimental investigation is worthwhile conducting.

Predicting Reliability and Remaining Useful Life of Rolling Bearings Based on Optimized Neural Networks

In this study,an optimized long short-term memory(LSTM)network is proposed to predict the reliability and remaining useful life(RUL)of rolling bearings based on an improved whale-optimized algorithm(IWOA).The multi-domain features are extracted to construct the feature dataset because the single-domain features are difficult to characterize the performance degeneration of the rolling bearing.To provide covariates for reliability assessment,a kernel principal component analysis is used to reduce the dimensionality of the features.A Weibull distribution proportional hazard model(WPHM)is used for the reliability assessment of rolling bearing,and a beluga whale optimization(BWO)algorithm is combined with maximum likelihood estimation(MLE)to improve the estimation accuracy of the model parameters of the WPHM,which provides the data basis for predicting reliability.Considering the possible gradient explosion by training the rolling bearing lifetime data and the difficulties in selecting the key network parameters,an optimized LSTM network called the improved whale optimization algorithm-based long short-term memory(IWOA-LSTM)network is proposed.As IWOA better jumps out of the local optimization,the fitting and prediction accuracies of the network are correspondingly improved.The experimental results show that compared with the whale optimization algorithm-based long short-term memory(WOA-LSTM)network,the reliability prediction and RUL prediction accuracies of the rolling bearing are improved by the proposed IWOA-LSTM network.

Research on Feature Extraction Method for Low-Speed Reciprocating Bearings Based on Segmented Short Signal Modulation Signal Bispectrum Slicing

Bearing condition monitoring and fault diagnosis (CMFD) can investigate bearing faults in the early stages, preventing the subsequent impacts of machine bearing failures effectively. CMFD for low-speed, non-continuous operation bearings, such as yaw bearings and pitch bearings in wind turbines, and rotating support bearings in space launch towers, presents more challenges compared to continuous rolling bearings. Firstly, these bearings have very slow speeds, resulting in weak collected fault signals that are heavily masked by severe noise interference. Secondly, their limited rotational angles during operation lead to a restricted number of fault signals. Lastly, the interference from deceleration and direction-changing impact signals significantly affects fault impact signals. To address these challenges, this paper proposes a method for extracting fault features in low-speed reciprocating bearings based on short signal segmentation and modulation signal bispectrum (MSB) slicing. This method initially separates short signals corresponding to individual cycles from the vibration signals based on encoder signals. Subsequently, MSB analysis is performed on each short signal to generate MSB carrier-slice spectra. The optimal carrier frequency and its corresponding modulation signal slice spectrum are determined based on the carrier-slice spectra. Finally, the MSB modulation signal slice spectra of the short signal set are averaged to obtain the overall average feature of the sliced spectra.

Influence of Vertical Irregularity on the Seismic Behavior of Base Isolated RC Structures with Lead Rubber Bearings under Pulse-Like Earthquakes

Nowadays,an extensive number of studies related to the performance of base isolation systems implemented in regular reinforced concrete structures subjected to various types of earthquakes can be found in the literature.On the other hand,investigations regarding the irregular base-isolated reinforced concrete structures’performance when subjected to pulse-like earthquakes are very scarce.The severity of pulse-like earthquakes emerges from their ability to destabilize the base-isolated structure by remarkably increasing the displacement demands.Thus,this study is intended to investigate the effects of pulse-like earthquake characteristics on the behavior of low-rise irregular base-isolated reinforced concrete structures.Within the study scope,investigations related to the impact of the pulse-like earthquake characteristics,irregularity type,and isolator properties will be conducted.To do so,different values of damping ratios of the base isolation system were selected to investigate the efficiency of the lead rubber-bearing isolator.In general,the outcomes of the study have shown the significance of vertical irregularity on the performance of base-isolated structures and the considerable effect of pulse-like ground motions on the buildings’behavior.

Analysis and optimization of stamping and forming process of bearing outer ring

To address the common issues of wrinkling,tearing,and uneven wall thickness in the actual sheet metal stamp-ing process of the outer ring of needle roller bearings,this study analyzes critical technical indicators such asforming limits,thickness distribution,and principal strains in the forming process in detail.Three-dimensionalmodels of the concave and convex dies were constructed.The effects of different process parameters,includingstamping speed,edge pressure,sheet metal thickness,and friction coefficient,on the quality of the forming partswere investigated by varying these parameters.Subsequently,the orthogonal experimental method was used todetermine an optimal experimental group from multiple sets of experiments.It was found that under the processparameters of a stamping speed of 3000 mm/s,edge pressure of 2000 N,sheet metal thickness of 0.9 mm,andfriction coefficient of 0.125,the forming quality of the outer ring of the bearing is ideal.

Theoretical Analysis on Deflection and Bearing Capacity of Prestressed Bamboo-Steel Composite Beams

A theoretical analysis of upward deflection and midspan deflection of prestressed bamboo-steel composite beams is presented in this study.The deflection analysis considers the influences of interface slippage and shear deformation.Furthermore,the calculation model for flexural capacity is proposed considering the two stages of loading.The theoretical results are verified with 8 specimens considering different prestressed load levels,load schemes,and prestress schemes.The results indicate that the proposed theoretical analysis provides a feasible prediction of the deflection and bearing capacity of bamboo-steel composite beams.For deflection analysis,the method considering the slippage and shear deformation provides better accuracy.The theoretical method for bearing capacity matches well with the test results,and the relative errors in the serviceability limit state and ultimate limit state are 4.95%and 5.85%,respectively,which meet the accuracy requirements of the engineered application.

A Dual-Task Learning Approach for Bearing Anomaly Detection and State Evaluation of Safe Region

Predictive maintenance has emerged as an effective tool for curbing maintenance costs,yet prevailing research predominantly concentrates on the abnormal phases.Within the ostensibly stable healthy phase,the reliance on anomaly detection to preempt equipment malfunctions faces the challenge of sudden anomaly discernment.To address this challenge,this paper proposes a dual-task learning approach for bearing anomaly detection and state evaluation of safe regions.The proposed method transforms the execution of the two tasks into an optimization issue of the hypersphere center.By leveraging the monotonicity and distinguishability pertinent to the tasks as the foundation for optimization,it reconstructs the SVDD model to ensure equilibrium in the model’s performance across the two tasks.Subsequent experiments verify the proposed method’s effectiveness,which is interpreted from the perspectives of parameter adjustment and enveloping trade-offs.In the meantime,experimental results also show two deficiencies in anomaly detection accuracy and state evaluation metrics.Their theoretical analysis inspires us to focus on feature extraction and data collection to achieve improvements.The proposed method lays the foundation for realizing predictive maintenance in a healthy stage by improving condition awareness in safe regions.

Fault Diagnosis Method of Rolling Bearing Based on MSCNN-LSTM

Deep neural networks have been widely applied to bearing fault diagnosis systems and achieved impressive success recently.To address the problem that the insufficient fault feature extraction ability of traditional fault diagnosis methods results in poor diagnosis effect under variable load and noise interference scenarios,a rolling bearing fault diagnosis model combining Multi-Scale Convolutional Neural Network(MSCNN)and Long Short-Term Memory(LSTM)fused with attention mechanism is proposed.To adaptively extract the essential spatial feature information of various sizes,the model creates a multi-scale feature extraction module using the convolutional neural network(CNN)learning process.The learning capacity of LSTM for time information sequence is then used to extract the vibration signal’s temporal feature information.Two parallel large and small convolutional kernels teach the system spatial local features.LSTM gathers temporal global features to thoroughly and painstakingly mine the vibration signal’s characteristics,thus enhancing model generalization.Lastly,bearing fault diagnosis is accomplished by using the SoftMax classifier.The experiment outcomes demonstrate that the model can derive fault properties entirely from the initial vibration signal.It can retain good diagnostic accuracy under variable load and noise interference and has strong generalization compared to other fault diagnosis models.

Novel Method for Evaluating the Aging of Aviation Turbine Engine Oils via High-Temperature Bearing Deposit Tests

Aviation turbine engine oils require excellent thermal-oxidative stability because of their high-temperature environments.High-temperature bearing deposit testing is a mandatory method for measuring the thermal-oxidative performance of aviation lubricant oils,and the relevant apparatus was improved in the present study.Two different commercial aviation turbine engine oils were tested,one with standard performance(known as the SL oil)and the other with high thermal stability,and their thermal-oxidative stability characteristics were evaluated.After 100 h of high-temperature bearing testing,the SL oil was analyzed by using various analytical techniques to investigate its thermal-oxidative process in the bearing test,with its thermal-oxidative degradation mechanism also being discussed.The results indicate that the developed high-temperature bearing apparatus easily meets the test requirements of method 3410.1 in standard FED-STD-791D.The viscosity and total acid number(TAN)of the SL oil increased with the bearing test time,and various deposits were produced in the bearing test,with the micro-particles of the carbon deposits being sphere-like,rod-like,and sheet-like in appearance.The antioxidant additives in the oil were consumed very rapidly in the first 30 h of the bearing test,with N-phenyl-1-naphthylamine being consumed faster than dioctyldiphenylamine.Overall,the oil thermal-oxidative process involves very complex physical and chemical mechanisms.

Influence of Incomplete Soil Plugs on Bearing Capacities of Bucket Foundations in Clay

Due to the uneven seabed and heaving of soil during pumping,incomplete soil plugs may occur during the installation of bucket foundations,and the impacts on the bearing capacities of bucket foundations need to be evaluated.In this paper,the contact ratio(the ratio of the top diameter of the soil plug to the diameter of the bucket)and the soil plug ratio(the ratio of the soil heave height to the skirt height)are defined to describe the shape and size of the incomplete soil plug.Then,finite element models are established to investigate the bearing capacities of bucket foundations with incomplete soil plugs and the influences of the contact ratios,and the soil plug ratios on the bearing capacities are analyzed.The results show that the vertical bearing capacity of bucket foundations in homogeneous soil continuously improves with the increase of the contact ratio.However,in normally consolidated soil,the vertical bearing capacity barely changes when the contact ratio is smaller than 0.75,while the bearing capacity suddenly increases when the contact ratio increases to 1 due to the change of failure mode.The contact ratio hardly affects the horizontal bearing capacity of bucket foundations.Moreover,the moment bearing capacity improves with the increase of the contact ratio for small aspect ratios,but hardly varies with increasing contact ratio for aspect ratios larger than 0.5.Consequently,the reduction coefficient method is proposed based on this analysis to calculate the bearing capacities of bucket foundations considering the influence of incomplete soil plugs.The comparison results show that the proposed reduction coefficient method can be used to evaluate the influences of incomplete soil plug on the bearing capacities of bucket foundations.