Ultra-Robust and High-Performance Rotational Triboelectric Nanogenerator by Bearing Charge Pumping

As an emerging technology to convert environmental high-entropy energy into electrical energy,triboelectric nanogenerator(TENG)has great demands for further enhancing the service lifetime and output performance in practical applications.Here,an ultra-robust and high-performance rotational triboelectric nanogenerator(R-TENG)by bearing charge pumping is proposed.The R-TENG composes of a pumping TENG(P-TENG),an output TENG(O-TENG),a voltage-multiplying circuit(VMC),and a buffer capacitor.The P-TENG is designed with freestanding mode based on a rolling ball bearing,which can also act as the rotating mechanical energy harvester.The output low charge from the P-TENG is accumulated and pumped to the non-contact O-TENG,which can simultaneously realize ultralow mechanical wear and high output performance.The matched instantaneous power of R-TENG is increased by 32 times under 300 r/min.Furthermore,the transferring charge of R-TENG can remain 95%during 15 days(6.4×10^(6)cycles)continuous operation.This work presents a realizable method to further enhance the durability of TENG,which would facilitate the practical applications of high-performance TENG in harvesting distributed ambient micro mechanical energy.

Seismic evaluation of frequency influence and resonant behavior for lead rubber bearing isolated rigid rectangular liquid tanks

The seismic behavior of a partially filled rigid rectangular liquid tank is investigated under short-and longduration ground motions.A finite element model is developed to analyze the liquid domain by using four-noded quadrilateral elements.The competency of the model is verified with the available results.Parametric studies are conducted for the dynamic parameters of the base-isolated tank,using a lead rubber bearing to evaluate the optimum damping and time period of the isolator.The application of base isolation has reduced the total and impulsive hydrodynamic components of pressure by 80 to 90 percent,and base shear by 15 to 95 percent,depending upon the frequency content and duration of the considered earthquakes.The sloshing amplitude of the base-isolated tank is reduced by 18 to 94 percent for most of the short-duration earthquakes,while it is increased by 17 to 60 percent for the majority of the long-duration earthquakes.Furthermore,resonance studies are carried out through a long-duration harmonic excitation to obtain the dynamic behavior of non-isolated and isolated tanks,using a nonlinear sloshing model.The seismic responses of the base-isolated tank are obtained as higher when the excitation frequency matches the fundamental sloshing frequency rather than the isolator frequency.

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.

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.

Establishment of the thermo-mechanical coupling model of axle box bearings with track irregularity excitation and analysis of its temperature characteristics

As an important component of the running gear of high-speed trains,axle box bearings can cause lubricating grease failure and damage to bearing components under continuous high-temperature operation,which will affect the normal operation of highspeed trains.Therefore,bearing temperature is one of the key parameters to be monitored in the online monitoring system for trains.Based on the thermal network method,this paper establishes a thermal network model for the axle box bearing,considering the radial thermal deformation of the double-row tapered roller bearing components caused by the oil film characteristics and the temperature variations of the lubricating grease.A thermo-mechanical coupling model for the grease-lubricated double-row tapered roller axle box bearing of high-speed trains with track irregularity excitation is established.The correctness of the model is verified using the test bench data,and the temperature of the bearing at different rotational speeds,loads,fault sizes,and ambient temperatures are investigated.

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 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.

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.

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.

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.

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.

Three dimensional discrete element modelling of the conventional compression behavior of gas hydrate bearing coal

To analyze the relationship between macro and meso parameters of the gas hydrate bearing coal(GHBC)and to calibrate the meso-parameters,the numerical tests were conducted to simulate the laboratory triaxial compression tests by PFC3D,with the parallel bond model employed as the particle contact constitutive model.First,twenty simulation tests were conducted to quantify the relationship between the macro–meso parameters.Then,nine orthogonal simulation tests were performed using four meso-mechanical parameters in a three-level to evaluate the sensitivity of the meso-mechanical parameters.Furthermore,the calibration method of the meso-parameters were then proposed.Finally,the contact force chain,the contact force and the contact number were examined to investigate the saturation effect on the meso-mechanical behavior of GHBC.The results show that:(1)The elastic modulus linearly increases with the bonding stiffness ratio and the friction coefficient while exponentially increasing with the normal bonding strength and the bonding radius coefficient.The failure strength increases exponentially with the increase of the friction coefficient,the normal bonding strength and the bonding radius coefficient,and remains constant with the increase of bond stiffness ratio;(2)The friction coefficient and the bond radius coefficient are most sensitive to the elastic modulus and the failure strength;(3)The number of the force chains,the contact force,and the bond strength between particles will increase with the increase of the hydrate saturation,which leads to the larger failure strength.

Bearing capacity of circular footings on multi-layered sand-waste tire shreds reinforced with geogrids

The presence of waste tires poses an environmental challenge as they occupy a significant amount of land and are expensive to dispose in landfills.However,reusing waste tires can address this issue when waste tires are used in geotechnical applications.To determine the viability of this approach,laboratoryscale tests were conducted to investigate load-bearing capacity of circular footings on sand-tire shred(STS)mixtures with shredded waste tire contents of 5%e15%by weight and three different widths of shreds.The investigation focused on analyzing the thickness of layers composed of STS mixtures,the soil cap,and the impact of geogrids on bearing capacity.The results indicate that a specific mixture of sand and tire shreds provides the highest footing-bearing capacity.In addition,the optimal shred content and size were found to be 10%by weight and 2 cm×10 cm,respectively.Furthermore,for a given tire shred width,a particular length provides the largest bearing capacity.The results agree well with that of previous research conducted by the first author and his colleagues in direct shear and California bearing ratio(CBR)tests.The primary finding of this research is that the use of two-layered STS mixtures reinforced by geogrids significantly enhances the bearing capacity.

Bearing mechanism of roof and rib support structure in automatically formed roadway and its support design method

Non-pillar mining technology with automatically formed roadway is a new mining method without coal pillar reservation and roadway excavation.The stability control of automatically formed roadway is the key to the successful application of the new method.In order to realize the stability control of the roadway surrounding rock,the mechanical model of the roof and rib support structure is established,and the influence mechanism of the automatically formed roadway parameters on the compound force is revealed.On this basis,the roof and rib support structure technology of confined lightweight concrete is proposed,and its mechanical tests under different eccentricity are carried out.The results show that the bearing capacity of confined lightweight concrete specimens is basically the same as that of ordinary confined concrete specimens.The bearing capacity of confined lightweight concrete specimens under different eccentricities is 1.95 times higher than those of U-shaped steel specimens.By comparing the test results with the theoretical calculated results of the confined concrete,the calculation method of the bearing capacity for the confined lightweight concrete structure is selected.The design method of confined lightweight concrete support structure is established,and is successfully applied in the extra-large mine,Ningtiaota Coal Mine,China.

Measured dynamic load distribution within the in situ axlebox bearing of high-speed trains under polygonal wheel–rail excitation

The dynamic load distribution within in-service axlebox bearings of high-speed trains is crucial for the fatigue reliability assessment and forward design of axlebox bearings. This paper presents an in situ measurement of the dynamic load distribution in the four rows of two axlebox bearings on a bogie wheelset of a high-speed train under polygonal wheel–rail excitation. The measurement employed an improved strain-based method to measure the dynamic radial load distribution of roller bearings. The four rows of two axlebox bearings on a wheelset exhibited different ranges of loaded zones and different means of distributed loads. Besides, the mean value and standard deviation of measured roller–raceway contact loads showed non-monotonic variations with the frequency of wheel–rail excitation. The fatigue life of the four bearing rows under polygonal wheel–rail excitation was quantitatively predicted by compiling the measured roller–raceway contact load spectra of the most loaded position and considering the load spectra as input.

Study on a conical bearing for acceleration-sensitive equipment

Seismic isolation effectively reduces seismic demands on building structures by isolating the superstructure from ground vibrations during earthquakes.However,isolation strategies give less attention to acceleration-sensitive systems or equipment.Meanwhile,as the isolation layer’s displacement grows,the stiffness and frequency of traditional rolling and sliding isolation bearings increases,potentially causing self-centering and resonance concerns.As a result,a new conical pendulum bearing has been selected for acceleration-sensitive equipment to increase self-centering capacity,and additional viscous dampers are incorporated to enhance system damping.Moreover,the theoretical formula for conical pendulum bearings is supplied to analyze the device’s dynamic parameters,and shake table experiments are used to determine the proposed device’s isolation efficiency under various conditions.According to the test results,the newly proposed devices have remarkable isolation performance in terms of minimizing both acceleration and displacement responses.Finally,a numerical model of the isolation system is provided for further research,and the accuracy is demonstrated by the aforementioned experiments.

Bearing Fault Diagnosis Based on the Markov Transition Field and SE-IShufflenetV2 Model

A bearing fault diagnosis method based on the Markov transitionfield(MTF)and SEnet(SE)-IShufflenetV2 model is proposed in this paper due to the problems of complex working conditions,low fault diagnosis accuracy,and poor generalization of rolling bearing.Firstly,MTF is used to encode one-dimensional time series vibration sig-nals and convert them into time-dependent and unique two-dimensional feature images.Then,the generated two-dimensional dataset is fed into the SE-IShufflenetV2 model for training to achieve fault feature extraction and classification.This paper selects the bearing fault datasets from Case Western Reserve University and Paderborn University to experimentally verify the effectiveness and superiority of the proposed method.The generalization performance of the proposed method is tested under the variable load condition and different signal-to-noise ratios(SNRs).The experimental results show that the average accuracy of the proposed method under different working conditions is 99.2%without adding noise.The accuracy under different working conditions from 0 to 1 HP is 100%.When the SNR is 0 dB,the average accuracy of the proposed method can still reach 98.7%under varying working conditions.Therefore,the bearing fault diagnosis method proposed in this paper is characterized by high accuracy,strong anti-noise ability,and generalization.Moreover,the proposed method can also overcome the influence of variable working conditions on diagnosis accuracy,providing method support for the accurate diagnosis of bearing faults under strong noise and variable working conditions.

Developing a novel big dataset and a deep neural network to predict the bearing capacity of a ring footing

The accurate prediction of the bearing capacity of ring footings,which is crucial for civil engineering projects,has historically posed significant challenges.Previous research in this area has been constrained by considering only a limited number of parameters or utilizing relatively small datasets.To overcome these limitations,a comprehensive finite element limit analysis(FELA)was conducted to predict the bearing capacity of ring footings.The study considered a range of effective parameters,including clay undrained shear strength,heterogeneity factor of clay,soil friction angle of the sand layer,radius ratio of the ring footing,sand layer thickness,and the interface between the ring footing and the soil.An extensive dataset comprising 80,000 samples was assembled,exceeding the limitations of previous research.The availability of this dataset enabled more robust and statistically significant analyses and predictions of ring footing bearing capacity.In light of the time-intensive nature of gathering a substantial dataset,a customized deep neural network(DNN)was developed specifically to predict the bearing capacity of the dataset rapidly.Both computational and comparative results indicate that the proposed DNN(i.e.DNN-4)can accurately predict the bearing capacity of a soil with an R2 value greater than 0.99 and a mean squared error(MSE)below 0.009 in a fraction of 1 s,reflecting the effectiveness and efficiency of the proposed method.