SELF-ALIGNING EVEN LOAD MECHANISM OF MULTI-ROW BEARINGS OF LARGE STRIP ROLLING MILL

The load distribution of multi-row bearings of large strip rolling mill is fully analyzed by 3D contact boundary element method （BEM）. It is found out that bearings are frequently worn out due to serious uneven load on the multi-row rollers. The constraint mechanism of the previous rolling system is found to be unreasonable by theoretical analysis on heavy machinery structure. A mechanism of self-aligning even load for workroll bearing of 2 050 mm hot rolling mill of Baoshan I＆S Co. is developed. This device is manufactured with particular regard to the structure of 2 050 mm hot rolling mill mentioned above. Hence, uneven load on multi-row bearings is greatly relieved and their lives are remarkably prolonged. Meanwhile, theoretical analysis and on-spot tests prove the rationality and validity of the device.

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.

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.

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.

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.

Centrifuge modeling of unreinforced and multi-row stabilizing piles reinforced landslides subjected to reservoir water level fluctuation

With the construction of the Three Gorges Reservoir dam,frequent reservoir landslide events have been recorded.In recent years,multi-row stabilizing piles(MRSPs)have been used to stabilize massive reservoir landslides in China.In this study,two centrifuge model tests were carried out to study the unreinforced and MRSP-reinforced slopes subjected to reservoir water level(RWL)operation,using the Taping landslide as a prototype.The results indicate that the RWL rising can provide lateral support within the submerged zone and then produce the inward seepage force,eventually strengthening the slope stability.However,a rapid RWL drawdown may induce outward seepage forces and a sudden debuttressing effect,consequently reducing the effective soil normal stress and triggering partial pre-failure within the RWL fluctuation zone.Furthermore,partial deformation and subsequent soil structure damage generate excess pore water pressures,ultimately leading to the overall failure of the reservoir landslide.This study also reveals that a rapid increase in the downslope driving force due to RWL drawdown significantly intensifies the lateral earth pressures exerted on the MRSPs.Conversely,the MRSPs possess a considerable reinforcement effect on the reservoir landslide,transforming the overall failure into a partial deformation and failure situated above and in front of the MRSPs.The mechanical transfer behavior observed in the MRSPs demonstrates a progressive alteration in relation to RWL fluctuations.As the RWL rises,the mechanical states among MRSPs exhibit a growing imbalance.The shear force transfer factor(i.e.the ratio of shear forces on pile of the n th row to that of the first row)increases significantly with the RWL drawdown.This indicates that the mechanical states among MRSPs tend toward an enhanced equilibrium.The insights gained from this study contribute to a more comprehensive understanding of the failure mechanisms of reservoir landslides and the mechanical behavior of MRSPs in reservoir banks.

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.

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.

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.

Analysis of Bridge-Bearing Capacity Detection and Evaluation Technology

A bridge project is taken as an example to analyze the application of bearing capacity detection and evaluation.This article provides a basic overview of the project,the application of bearing capacity detection technology,and the bearing capacity assessment analysis.It is hoped that this analysis can provide a scientific reference for the load-bearing capacity detection and evaluation work in bridge engineering projects,thereby achieving a scientific assessment of the overall load-bearing capacity of the bridge engineering structure.

Analysis of the Application of Static Load Test in Bridge Bearing Capacity Testing

This article uses real engineering projects as examples to analyze how static load test technology is applied in testing the bridge-bearing capacity.The analysis covers aspects such as testing section layout,test load and efficiency coefficient,loading plan,evaluation optimization,test result modification,and result evaluation.The aim is to support the accurate detection and evaluation of bridge-bearing capacity.