Rolling Bearing System Research Articles

Numerical Assessment of Bidirectional Roller Bearing Isolators under Near-Fault Earthquakes

In this research, a bidirectional seismic isolation device composed of sloped bearing plates and multiple rollers arranged in both orthogonal-in-plane directions is numerically studied. A previous analytical model of a single direction of roller bearing (RB) system is extended to a two-direction RB system. Several experimental tests in a physical building model with and without an RB system are used to validate the numerical model. The model is used to perform the nonlinear response of a four-story multi-column building when subjected to pairs of scaled near-fault earthquake records. The effect of the inclination angle of bearing plates in the range of 1.0° to 4.0°, the sliding friction force, and supplementary damping mechanisms ranging from 0.0 to 0.5 N/kg (i.e. friction force normalized with the structure mass) are also investigated. The results show that the proposed bidirectional RB system is suitable for reducing the seismic response of stiff and flexible multi-column structures. In particular, the RB system reduces the acceleration responses by 5% to 85% in flexible structures and 86% to 96% in stiff structures. Furthermore, bearing plates with an inclination angle greater than or equal to 3.0° have significant benefits in terms of self-centering capacity.

A novel stochastic resonance based deep residual network for fault diagnosis of rolling bearing system

Rolling bearings constitute one of the most vital components in mechanical equipment, monitoring and diagnosing the condition of rolling bearings is essential to ensure safe operation. In actual production, the collected fault signals typically contain noise and cannot be accurately identified. In the paper, stochastic resonance (SR) is introduced into a spiking neural network (SNN) as a feature enhancement method for fault signals with varying noise intensities, combining deep learning with SR to enhance classification accuracy. The output signal-to-noise ratio(SNR) can be enhanced with the SR effect when the noise-affected fault signal input into neurons. Validation of the method is carried out through experiments on the CWRU dataset, achieving classification accuracy of 99.9%. In high-noise environments, with SNR equal to −8 dB, SRDNs achieve over 92% accuracy, exhibiting better robustness and adaptability.

Diagnosis of rotating machinery based on improved convolutional neural networks with gray-level transformation

A fault diagnosis method for the rotating machinery based on improved Convolutional Neural Network (CNN) with Gray-Level Transformation (GLT) is proposed to increase the accuracy of the recognition adopting the multiple sensors. The Symmetrized Dot Pattern (SDP) in this method is applied to fuse the data of the multiple sensors, and the multi-color value method is adopted to increase the feature dimension. The grayscale and GLT are used to reduce the dimension of the SDP image. The SDP grayscale image is finally input to the CNN network for training recognition. The research results show that the diagnosis accuracy of the rolling bearing system based on the novel method is up to 98.6 %. Compared with the method without the multi-color value and GLT, the recognition accuracy of the proposed method is improved by 22.3 %, and the training time is reduced by about one third. The research work reveals that the developed method has the potential application value under the multi-sensor working conditions for the fault diagnosis.

Experimental investigations to analyze surface contact fatigue wear by using a dynamic response of the roller bearing system

Experimental investigations to analyze surface contact fatigue wear by using a dynamic response of the roller bearing system

Design and Prototype Production of Scissor Lift Platform 25 Tons Capacity

In this study, an innovative 25-ton-capacity scissor lift was designed for the first time, and a prototype was produced in Onder Group Inc. Within the scope of the study, a rolling bearing system was designed instead of the conventional welded scissor-hinge connection system. Static and strength calculations were made using finite element analysis for comparison purposes. As a result of the analysis, it was determined that the stress distribution on the roller bearing system was more homogeneous and at lower values (≈60 MPa) than the welded hinge system. In addition, a detachable (bolted) joint was obtained instead of a fixed (welded) joint with the designed rolling bearing system. With the design analyses carried out within the scope of the study, a particular joint-clamp system was made to expand the surface areas of the welded joints.

Dynamic Analysis of Outer and Inner Race Defects on Thermoplastic Rolling Bearing System Using Implicit Finite Element Method

This paper proposes an improved finite element dynamic model to analyze the vibration response of a rolling bearing system. The vibration responses of defect free and defective polypropylene (PP) bearings are analyzed using the finite element analysis and compared with the experimental results using the in house developed bearing fatigue test rig. The boundary conditions of this model are imposed to ensure an adequate homogeneity with the experimental apparatus. This study considers the viscoelastic property of thermoplastic to investigate the effect of the flexibility and damping viscosity of material. The three-dimensional dynamic analysis detects the vibrations produced in a rolling bearing system. Modal frequencies and the vibration modes shapes which must be avoided had been determined. Monitoring the evolution of vibration signatures as a function of defect location is also carried out using Finite Element Analysis (FEM). Test results reveal that peak vibration amplitudes are more pronounced in an inner ring defect than in an outer ring defect. Vibration signals of stainless-steel bearings are investigated to compare the performance of the thermoplastic bearings with its metallic counterparts. Both test and simulation results reveal that a lower level of vibration is observed with PP bearings compared to that of metal. The PP bearings not only dampen vibrations, but also accommodate shaft misalignments unlike their steel counterparts. Once overall vibration spectrums results are validated, Von Mises stresses within the rings are evaluated under excessive loading conditions. The simulation results show that stress is high in raceways where the balls are compressed between the raceways. Computational results agree well with the experimental tests for the test scenarios.

Dynamics simulation of rolling bearing system with pedestal looseness in speed-up train

The paper mainly studies the dynamics characteristics of the roller bearing system with pedestal looseness in speed-up train. Considering eccentricity, damping coefficient, stiffness of shaft, and support forces of roller bearing, a centralized mass model of roller bearing system of speed-up train is presented. Moreover, numerical analysis method is adopted to solve the dynamics responses. Based on the trajectory cures, phase plane portraits, Poincaré maps, and amplitude spectrum cures, the dynamics characteristics are analysed when the motor rotating speed and gap of the looseness are changed. The simulation results reveal some complex dynamic behaviour such as periodic motion, quasi-periodic motion, multi-periodic motion and chaotic motion. These results will help us understand and harness these dynamics behaviour of the proposed bearing system of speed-up train.

Applications of vibro-acoustic measurement and analysis in conjunction with tribological parameters to assess surface fatigue wear developed in the roller-bearing system

Rolling-element bearings are the most commonly used components in all rotating machinery. The variations in the operating conditions such as an increase in the number of operating cycles, load, speed, service temperature, and lubricant degradation result in the development of various defects such as pitting, spalling, scuffing, scoring, etc. The defects that appeared on rolling contact surfaces cause surface deterioration and change in the vibration and sound levels of the bearing system. The present experimental investigations are aimed at assessing the surface fatigue wear that appears on the contact surfaces of roller bearings. The studies considered the estimation of specific film thickness, analysis of surface fatigue wear developed on the rolling-element surfaces, surface roughness analysis, grease degradation analysis using Fourier transform infrared radiation, and vibration and sound signal measurement and analysis. The results obtained from the experimental investigation provide a good correlation between surface wear, vibration, and sound signals with a transition in the lubrication regimes in the Stribeck curve.

A chaotic system-based signal identification Technology: Fault-diagnosis of industrial bearing system

A set of smart fault-diagnosis technology with features production, extraction, and selection is developed via applying data-mapping strategy and K-Nearest Neighbor (K-NN) algorithm. By designing a nonlinear chaotic mathematics model consisting of a main system and a data-feed system with appropriate parameter settings, the raw vibration signals from time domain can be mathematically mapped into chaotic domain for potential features production, where those tiny differences between each of data set can be clearly observed in the chaotic domain. Further, through appropriate key-features extraction approaches-Euclidean Feature Values (EFV), Squared Euclidean Feature Values (SEFV), and the projections in the × ,y and z directions, the related scatter plots of those features can be employed for key-features selections. In the final step, the classification results as well as their corresponding accuracy rates are investigated via K-NN algorithm. The different fault-states of industrial commonly-used rotating machine, rolling-bearing system, are illustrated for performance evaluations. In order to show the well-performance of the proposed technology, three fault conditions with diameter of 7 mil, 14 mil, 21 mil and a depth of 11 mil are all discussed. Experimental results demonstrate that the proposed smart fault-diagnosis technology is effective and feasible for real-time fault-diagnosis, where only K = 1 can be applied to identify different fault states in the fault conditions of 7 mil and 21 mil, the accuracy rate achieve around 100%. Further, for the fault conditions of 14 mil, 97.48 and 97.45% accuracy rate can be obtained via using K = 17 and K = 21. The average accuracy rate of the proposed smart detection system for the open as well as public database is around 99.16%

Intelligent fault diagnosis of rolling bearing and gear system under fluctuating load conditions using image processing technique

Health monitoring of a rotating machine is mainly done by investigation of the vibration patterns generated by the machine. Leveraging the fact that faults occurring in different parts of a machine generate unique fault signatures, a fault diagnosis methodology is proposed that can identify nine different healthy and faulty categories under varying load and noisy conditions. Neural network is employed for classification of faults in various categories. The robustness of features such as semivariance, kurtosis and Shannon entropy make them strong candidates to train the artificial neural network. The matching of vibration textural patterns with wave atom basis functions ensures removal of noise. As a result, the enhanced features used to train the neural network have led to high accuracy in classification. The algorithm is tested at various load conditions for both bearing and gear fault experimental data sets acquired by machinery fault simulator in laboratory. Simulation results show high degree of accuracy for both bearing and gear fault diagnosis under no load to heavy load noisy conditions.

Fault diagnosis of rolling element bearing weak fault based on sparse decomposition and broad learning network

Rolling element bearings are widely used in rotating machinery and, at the same time, they are easily damaged due to harsh operating environments and conditions. As a result, rolling element bearings are critical to the safe operation of the mechanical devices. The incipient fault information extraction of rolling bearings mainly faces the following difficulties: (1) The fault signal is too weak. (2) The fault mechanism and the dynamic model of the rolling bearing system are complex. (3) The oscillations caused by the fault shocks are overlapped due to the smaller impact between two adjacent faults. (4) The impact interval of the fault will change randomly. To overcome the aforementioned difficulties, a connection network constructed by resonance-based sparse signal decomposition (RSSD) and broad learning system (BLS) without the need for deep architecture, namely RSSD-BLS, is proposed for intelligent fault diagnosis. We construct RSSD-BLS by input layer, RSSD decomposition layer, feature layer and output layer. So, when the observed vibration signals are the input layer, the network first uses RSSD to decompose the raw vibration signal into high resonance components and low resonance components. Then, the network obtains energy spectrum features of high resonance components which decomposed by RSSD to extract the unique features in the feature. Finally, the network recognizes different fault conditions in the output layer. Through comparing with commonly used intelligent network diagnosis method, the superiority of the proposed RSSD-BLS is verified.

Effects of surface texturing on the tribological performance of the rolling bearing system

Surface texturing is one important means to improve the tribological performance of friction pairs. Meanwhile, the friction of the rolling bearing is related to the sliding behavior of the contact surfaces. At present, there are few research work on the textured rolling bearings. Therefore, it would be meaningful to study the potential of surface texturing in improving the performance of rolling bearing system. This paper developed one EHL (elasto-hydrodynamic lubrication) model for the rolling bearing system. In particular, the roller/outer ring conjunction was used as the research object. Effects of surface texturing on the tribological performance of the rolling bearing systems were studied under different bearing speeds. As a main conclusion, the local oil film thickness of the rolling bearing system can be increased after surface texturing. It seems that the textured surface can obtain a special benefit on the tribological properties of rolling bearing system.

Multisensor signal denoising based on matching synchrosqueezing wavelet transform for mechanical fault condition assessment

Since it is difficult to obtain the accurate running status of mechanical equipment with only one sensor, multisensor measurement technology has attracted extensive attention. In the field of mechanical fault diagnosis and condition assessment based on vibration signal analysis, multisensor signal denoising has emerged as an important tool to improve the reliability of the measurement result. A reassignment technique termed the synchrosqueezing wavelet transform (SWT) has obvious superiority in slow time-varying signal representation and denoising for fault diagnosis applications. The SWT uses the time–frequency reassignment scheme, which can provide signal properties in 2D domains (time and frequency). However, when the measured signal contains strong noise components and fast varying instantaneous frequency, the performance of SWT-based analysis still depends on the accuracy of instantaneous frequency estimation. In this paper, a matching synchrosqueezing wavelet transform (MSWT) is investigated as a potential candidate to replace the conventional synchrosqueezing transform for the applications of denoising and fault feature extraction. The improved technology utilizes the comprehensive instantaneous frequency estimation by chirp rate estimation to achieve a highly concentrated time–frequency representation so that the signal resolution can be significantly improved. To exploit inter-channel dependencies, the multisensor denoising strategy is performed by using a modulated multivariate oscillation model to partition the time–frequency domain; then, the common characteristics of the multivariate data can be effectively identified. Furthermore, a modified universal threshold is utilized to remove noise components, while the signal components of interest can be retained. Thus, a novel MSWT-based multisensor signal denoising algorithm is proposed in this paper. The validity of this method is verified by numerical simulation, and experiments including a rolling bearing system and a gear system. The results show that the proposed multisensor matching synchronous squeezing wavelet transform (MMSWT) is superior to existing methods.

Defect diagnostics of roller bearing using instantaneous frequency normalization under fluctuant rotating speed

We investigated the feasibility of utilizing the normalized characteristic frequencies for diagnosing the defective roller bearings in case of fluctuant rotating speeds. The time-frequency distributions of the envelope signals of the vibration data were constructed through the Empirical mode decomposition (EMD) as well as the instantaneous frequency calculation. The bearing defect-related frequencies were then normalized with respect to the instantaneous rotation frequency of the shaft so that the factor of the rotating speed fluctuation was removed; thus the characteristic frequencies of bearing malfunctions could be observed in terms of constant values. The magnitude distributions of the marginal envelope spectra at the corresponding normalized bearing defect-related frequencies were extracted as the feature vectors. The Support vector machine (SVM) was used to classify the extracted feature vectors of different bearing fault classes. A test rig of roller bearing system was performed to illustrate the different bearing faults, including different levels of inner race defect, outer race defect and roller defect. The analysis results demonstrate the capability and effectiveness of the proposed approach for accurately identifying the bearing defects in case of fluctuant rotating speed.

Neimark-Sacker Bifurcation of Rolling Bearing System with Fault in Outer Ring

Piecewise non-smooth model of three-degree-of-freedom rolling bearing system with fault in outer Ring is established by the method of the nonlinear theory. The Neimark-Sacker bifurcation of bearing system is first studied in this paper. The switching matrixes of system are obtained at the switching boundaries, and theNeimark-Sacker bifurcation of non-smooth bearing system is analyzed by combining the switching matrixes with the Floquet theory for smooth systems. The numerical method is used to further reveal the Neimark-Sacker bifurcation of bearing system through establishing the Poincare mapping on the collision plane. When the rotating frequency is increased to reach the critical bifurcation point, a pair of complex conjugate Floquet multipliers is on the unit circle and others into a unit circle , and the Neimark-Sacker bifurcation appears.The study of bifurcation of the fault bearing system provides reliable basis for the design and fault diagnosis and provides theoretical guidance and technical support for the actual design in the safe and stable operation of large high-speed rotating machinery.

Fault detection of a roller-bearing system through the EMD of a wavelet denoised signal.

This paper investigates fault detection of a roller bearing system using a wavelet denoising scheme and proper orthogonal value (POV) of an intrinsic mode function (IMF) covariance matrix. The IMF of the bearing vibration signal is obtained through empirical mode decomposition (EMD). The signal screening process in the wavelet domain eliminates noise-corrupted portions that may lead to inaccurate prognosis of bearing conditions. We segmented the denoised bearing signal into several intervals, and decomposed each of them into IMFs. The first IMF of each segment is collected to become a covariance matrix for calculating the POV. We show that covariance matrices from healthy and damaged bearings exhibit different POV profiles, which can be a damage-sensitive feature. We also illustrate the conventional approach of feature extraction, of observing the kurtosis value of the measured signal, to compare the functionality of the proposed technique. The study demonstrates the feasibility of wavelet-based de-noising, and shows through laboratory experiments that tracking the proper orthogonal values of the covariance matrix of the IMF can be an effective and reliable measure for monitoring bearing fault.

Rolling Bearing Fault Diagnosis Based on CEEMD and Time Series Modeling

Accurately identifying faults in rolling bearing systems by analyzing vibration signals, which are often nonstationary, is challenging. To address this issue, a new approach based on complementary ensemble empirical mode decomposition (CEEMD) and time series modeling is proposed in this paper. This approach seeks to identify faults appearing in a rolling bearing system using proper autoregressive (AR) model established from the nonstationary vibration signal. First, vibration signals measured from a rolling bearing test system with different defect conditions are decomposed into a set of intrinsic mode functions (IMFs) by means of the CEEMD method. Second, vibration signals are filtered with calculated filtering parameters. Third, the IMF which is closely correlated to the filtered signal is selected according to the correlation coefficient between the filtered signal and each IMF, and then the AR model of the selected IMF is established. Subsequently, the AR model parameters are considered as the input feature vectors, and the hidden Markov model (HMM) is used to identify the fault pattern of a rolling bearing. Experimental study performed on a bearing test system has shown that the presented approach can accurately identify faults in rolling bearings.

Nonlinear Dynamics Behaviors of a Rotor Roller Bearing System with Radial Clearances and Waviness Considered

A rotor system supported by roller bearings displays very complicated nonlinear behaviors due to nonlinear Hertzian contact forces, radial clearances and bearing waviness. This paper presents nonlinear bearing forces of a roller bearing under four-dimensional loads and establishes 4-DOF dynamics equations of a rotor roller bearing system. The methods of Newmark-β and of Newton-Laphson are used to solve the nonlinear equations. The dynamics behaviors of a rigid rotor system are studied through the bifurcation, the Poincarè maps, the spectrum diagrams and the axis orbit of responses of the system. The results show that the system is liable to undergo instability caused by the quasi-periodic bifurcation, the periodic-doubling bifurcation and chaos routes as the rotational speed increases. Clearances, outer race waviness, inner race waviness, roller waviness, damping, radial forces and unbalanced forces—all these bring a significant influence to bear on the system stability. As the clearance increases, the dynamics behaviors become complicated with the number and the scale of instable regions becoming larger. The vibration frequencies induced by the roller bearing waviness and the orders of the waviness might cause severe vibrations. The system is able to eliminate non-periodic vibration by reasonable choice and optimization of the parameters.

Complexity as a Measure for Machine Health Evaluation

This paper presents a machine health evaluation technique using the Lempel-Ziv complexity as a numerical measure. Comparing to conventional techniques such as spectral and time-frequency analysis, the presented approach does not require a linear transfer function of the physical system to be evaluated, and is thus suited for the condition monitoring of machine systems under varying operation and loading conditions. Theoretical foundation of the technique is introduced, and its performance is investigated through experimental study of realistic vibration signals measured from a rolling bearing system. The results demonstrated that complexity provides an effective measure for machine health condition evaluation.

Fabrication of silicon nitride bilayer for roller bearing by plasma activated sintering

Silicon nitride bilayers with the α-phase and the β-phase as outer and inner layers, respectively, were prepared by the plasma activated sintering (PAS) method, under the conditions of 30 MPa and 1 min in nitrogen atmosphere at 1550 °C for the α-phase and 2 min at 1700 °C for the β-phase. The underlying objective was microstructural design of the roller bearing system with optimum mechanical properties; alternating hard layers, for wear resistance, with tough layers, for fracture resistance. Hardness values through Vickers indentation tests on cross-sections of specimen demonstrated the possibility of the thickness control of the bilayer. Fracture toughness of the bilayer with the thickness of the α-phase layer was changed, indicating that the β-phase affected fracture resistance. Implications concerning the fabrication of silicon nitride bilayer for roller bearing application were considered.