Different Types Of Bearings Research Articles

Seismic analysis of 3D train–wheel–bridge–bearing systems: Modeling

AbstractBearing effects have not been deeply investigated in the dynamic analysis of a train–bridge coupled system subjected to earthquake motions, and existing studies on base isolation of railway bridges have typically not considered the running train. To fill these gaps, this paper develops the numerical model of a three‐dimensional train–wheel–bridge–bearing (TWBB) system for seismic analysis. The substructuring method is implemented to separate the TWBB system into the train, wheel–rail, bridge and bearing subsystems, and couple them using state‐space representations. The complete model of the TWBB system has three main features: fewer degrees of freedom (DOF) than traditional finite element methods, applicability to different types of bearings, and simplicity as no iterations in the simulation are required. Therefore, the proposed procedure demonstrates that the TWBB model is an efficient and flexible representation to obtain and predict realistic demands for engineering purposes.

A novel two-dimensional exponential potential bi-stable stochastic resonance system and its application in bearing fault diagnosis

It is difficult for classical stochastic resonance systems to extract weak signals under strong noise environment, therefore a novel two-dimensional exponential potential bi-stable stochastic resonance system (NTBSR) is proposed. First, the equivalent potential function, the mean first-pass time (MFPT) and the output signal-to-noise ratio (SNR) of NTBSR are derived under the adiabatic approximation theory. At the same time, the influence of different system parameters on them is explored. Then, NTBSR, the one-dimensional bi-stable stochastic resonance system (OBSR) and the two-dimensional classical bi-stable stochastic resonance system (TCBSR) are respectively simulated numerically, based on the fourth-order Runge–Kutta algorithm. It is found that the output SNR of NTBSR is the best. Finally, the NTBSR is applied to the fault signal diagnosis of different types of bearings, and the parameters are optimized through the adaptive genetic algorithm (AGA). The test results are compared with wavelet transform method, and TCBSR. The detection results on two sets of bearing fault data show that the NTBSR system has better effects on the enhancement and detection of bearing fault signals, and it is verified that the stochastic resonance method is superior to the traditional wavelet transform method in terms of signal detection and noise utilization. This provides good theoretical support and application value for practical engineering application.

A novel coupled two-dimensional unsaturated asymmetric bistable stochastic resonance system for bearing fault detection

A novel coupled two-dimensional unsaturated asymmetric bistable stochastic resonance (NCTUABSR) method is proposed in this paper, which is formed by coupling a new asymmetric bistable controlled system with a monostable control system, and significantly solves the serious output saturation problem of traditional two-dimensional bistable system. Based on the SPD and MFPT, it is found that with the increase of coupling, the depth and distance of double potential wells in two-dimensional space will decrease, which makes the particles more prone to inter-well transition. Combined with SNR, the performance characteristics of NCTUABSR under the influence of different parameters are deeply analyzed. NCTUABSR is applied to different types of bearings for fault signal detection, and adaptive genetic algorithm is used to optimize the parameters. Compared with different coupling systems, it is found that the detection performance of NCTUABSR has the most advantages, indicating that the system has obvious feasibility in practical engineering applications.

Study of sensitivity and interference protection of various bearing diagnostics methods

Due to the widespread use of bearing units with rolling bearings in ship equipment, the question of using diagnostics-in-place methods to determine their technical condition is relevant. For diagnostics, various methods of different sensitivity and interference protection can be applied. These qualities are of particular importance for diagnosing the bearing units of ship’s gearboxes access to which can be difficult, and the units themselves include several different types of bearings. The article provides materials on comparing the sensitivity and reliability of two methods for diagnosing rolling bearings: the diagnostic method based on the analysis of the envelope spectrum and diagnostic method based on shock pulses. Measurements were taken by these methods at different points of the bearing unit on the special bench, equipped with two different bearings. Previously, defects on the ring were artificially created on one of the bearings of the unit. Measurements have shown that the shock pulse method provides acceptable results only when a sensor is located directly at the point adjacent to the outer ring of the bearing. At the control points distant from the bearing location, the defect was diagnosed with an error, but the presence of a defect, nevertheless, could be determined by applying monitoring at a specific point of control. The method of analyzing the envelope allows secure detecting and identifying the defect even when a sensor is installed at a considerable distance from the bearing. The influence of the interference generated by the mounted lubrication pump is considered. The envelope spectra and the nature of their changes are provided.

Experimental study of hemispheric rolling cable-strut joints for cable dome applications

Cable domes achieve structural stiffness and loading capacity through tension cables and compression struts. Friction from cable-strut joints contributes a major prestress loss during tensioning. This paper presents a novel hemispheric rolling cable-strut joint which provides smooth load transmission and high prestress efficiency. It comprises a hemispheric cover plate, a bottom plate, a roller, and top and bottom bearings. The bearings embedded in the roller reduce friction transmission from the tensioned cable; thus, prestress loss is minimized. A theoretical analysis of load transmission and prestress loss is conducted for the cable-strut joint, followed by an experimental study. The experiments test cable-strut joints with different types of bearings, using a fixed-roller joint as a reference, and are shown to validate the theoretical approach. A potential method to improve the prestress efficiency during tensioning is discussed. Prestress loss rates are compared for the test joints and an optimum bearing type is recommended in consideration of both friction coefficients and service time. The hemispheric rolling cable-strut joint accommodates complex cable and strut configurations with clear load path and minimal prestress loss. Our results show that it provides a high-performance solution that can be used to improve the structural stability and stiffness of next generation long-span spatial structures.

Research and application of a novel piecewise unsaturated asymmetric tristable stochastic resonance system

Early fault diagnosis of rolling bearings is of great significance in the application of mechanical equipment, which makes the extraction of weak fault signals particularly critical by stochastic resonance (SR). Compared with bistable SR, tristable SR has stronger advantages in weak signal extraction, but the classical tristable stochastic resonance (CTSR) system was limited by output saturation, which resulted in insufficient signal amplification ability. To solve the above problems, combined with asymmetric system whose output can be improved to a higher degree, a novel piecewise unsaturated asymmetric tristable SR (NPUATSR) system is proposed. Through numerical simulation, it is concluded that NPUATSR output amplitude varies proportionally with the amplitude of the input, which overcomes the output saturation of CTSR. Secondly, the stationary probability density and mean first passage time of particles are derived by using adiabatic approximation theory, and the variation law caused by parameters is analyzed in combination with potential function, the internal mechanism of the system is further studied. Through the output signal-to-noise ratio (SNR), it is found that the performance advantage of NPUTASR system is the most obvious, and different parameters affect the output SNR. Finally, the adaptive genetic algorithm is used to optimize the parameters, and the proposed system is applied to early fault diagnosis on different types of bearings. After comparison with different systems, the results show that NPUATSR can effectively detect the fault frequency, and has the most outstanding advantages in spectrum amplification and anti-noise performance, which proves that NPUATSR system has significant value in practical engineering application.

Collection and processing of bearing vibration data for their technical condition classification by machine learning methods

An experimental research facility has been developed to receive vibration signals from mechanisms with installed rolling bearings. A control block for all equipment has been created. For the repeatability of the experiment, an external microcontroller with a programmed proportional-integral-derivative regulator was used.
 Experiments were carried out to obtain initial data for different types of bearings. The processed data were grouped and made publicly available for further research. It is proposed to solve the problem of emergency stop of the generator, arising during operation due to bearings worn, by recognizing the pre-emergency conditions of rotary rig based on the use of advanced machine learning techniques: to highlight the signs of vibration and build clusters according to the degree of worn.

Bearing fault diagnosis with intermediate domain based Layered Maximum Mean Discrepancy: A new transfer learning approach

In the last decade, deep learning models for condition monitoring of mechanical systems increasingly gained importance. Most of the previous works use data of the same domain (e.g., bearing type) or of a large amount of (labeled) samples. This approach is not valid for many real-world scenarios from industrial use-cases where only a small amount of data, often unlabeled, is available.In this paper, we propose, evaluate, and compare a novel technique based on an intermediate domain, which creates a new representation of the features in the data and abstracts the defects of rotating elements such as bearings. The results based on an intermediate domain related to characteristic frequencies show an improved accuracy of up to 32 % on small labeled datasets compared to the current state-of-the-art in the time-frequency domain.Furthermore, a Convolutional Neural Network (CNN) architecture is proposed for transfer learning. We also propose and evaluate a new approach for transfer learning, which we call Layered Maximum Mean Discrepancy (LMMD). This approach is based on the Maximum Mean Discrepancy (MMD) but extends it by considering the special characteristics of the proposed intermediate domain. The presented approach outperforms the traditional combination of Hilbert–Huang Transform (HHT) and S-Transform with MMD on all datasets for unsupervised as well as for semi-supervised learning. In most of our test cases, it also outperforms other state-of-the-art techniques.This approach is capable of using different types of bearings in the source and target domain under a wide variation of the rotation speed.

Influence Assessment of the Information Concerning the Bearing Type of Axle Box of Cars on the Automatic Classification Accuracy of Heated Boxes

Purpose. The main purpose of the work is to determine and classify the heated cars’ boxes based on the probability of appearance of roller and cassette type boxes in the classes of heated and overheated boxes, as well as the laws of probability density distribution of the recognition signs of normally heated and overheated roller and cassette type boxes. Methodology. The operation features of freight cars with cassette type axle boxes with increased operating heating have been investigated. The methodology of assessing the probability of recognition errors was proposed, which takes into account the fact that sets of normally heated and overheated boxes consist of subsets of boxes with different types of bearings. A system of equations is obtained, the roots of which represent еру values that minimize the recognition probability of the errors of the heated boxes. Findings. It was found out that with some methods of determining the bearing type, for example, by the average value of the ranges of thermal image for each car, the probability of erroneous selection may depend on the probability density distribution of the sign for bearings of different types and the threshold value of this sign. The optimal thresholds for detecting the overheated roller boxes in comparison with the optimal thresholds for detecting overheated cassette boxes were determined. It has been established that the pass of an overheated cassette bearing, provided that the type of bearing is determined correctly, is less likely to lead to an accident than if the cassette box is classified as a roller box. The rejection criteria of axle boxes according to their heating temperature difference on one of the wheel set axis for three variants of settings of the alarm system according to an arrangement of multipurpose complexes of technical means (CTM) were formulated. The practical implementation of this method of adjusting the CTM settings for the Minsk branch of the Belarusian Railways was demonstrated. Originality. A system of equations is obtained, which allows finding the optimal values of temperature thresholds for the detection of overheated roller and cassette boxes under the assumption that the error probabilities in the selection of boxes by their types are known and constant. Practical value. The developed method of adjusting the alarm settings of CTM makes it possible to significantly reduce unjustified train delays and the number of car uncouplings.

Piecewise asymmetric exponential potential under-damped bi-stable stochastic resonance and its application in bearing fault diagnosis

It is difficult to extract weak signals in strong noise background, therefore a piecewise asymmetric exponential potential under-damped bi-stable stochastic resonance (PAEUBSR) system is proposed. First, the theoretical analysis of the steady-state probability density (SPD), mean first passage time (MFPT) and output signal-to-noise ratio (SNR) are derived under the adiabatic approximation theory. At the same time, the influence of different system parameters on system performance is explored. Then the PAEUBSR system is applied to the fault signal diagnosis of different types of bearings, and the parameters are optimized through the adaptive genetic algorithm (AGA). The test results are compared with the exponential potential over-damped symmetric bi-stable stochastic resonance (EOSBSR) system and the exponential potential under-damped symmetric bi-stable stochastic resonance (EUSBSR) system. Finally, the detection results on two sets of bearing fault data show that the PAEUBSR system has better effects on the enhancement and detection of bearing fault signals. This provides good theoretical support and application value for this system in subsequent theoretical analysis and practical engineering applications.

FEA Modelling of the Combined Hard TurningRolling Process Used at Bearing Rings

In general, the production of bearings is a very large series production, which is why in production the technological lines are designed to process a single size of bearings. Changing the production line for different types of bearings is expensive and time consuming, especially where grinding and honing processes are required. An alternative to these abrasive processes is hard turning. The literature indicates that due to highprecision machines, the accuracy of hard-turned parts is comparable to grinding processes. It is also indicated that the integrity of the surface and the topography of the surface together with the residual induced stresses are parameters of interest and that influence the performance of the bearings. So one method of increasing the durability of the bearings is to ensure a low roughness of the elements and high residual induced stresses. Deep rolling is considered as an alternative to honing and rectification processes. Rolling can induce higher surface stresses in the material compared to honing and grinding. The present paper proposes a combined cutting tool made of a hard turning head and a rolling cutting tool for machining bearing rings. A simulation of this combined process is performed with the help of the finite element and thus the internal stress field, the temperature fieldand the topography of the processed surface aredetermined.

Feature extraction for bearing prognostics using weighted correlation of fault frequencies over cycles

In the predictive maintenance of rolling element bearings, finding a good prognostic feature to predict the remaining useful life is of critical importance, since it enables the maintenance scheduling in advance while ensuring the life without failure. In this article, a method for health index extraction is proposed for the bearing prognostics using the weighted correlation of fault frequencies over cycles. Although there have been numerous studies toward this purpose, this article is distinct in three aspects, which is more favorable for practical applications: First, the feature is simple and is rooted on the physical faults. Second, the method is applied after the anomaly detection. Third, the method is examined by the three run-to-failure cases with different types of bearings: one ball and two roller elements, which may suggest more general applicability. The trends of the proposed health index and prognostic performance are evaluated against the traditional features: root mean square and kurtosis. As a result, it is found that the new health index shows superior performance in view of the prognosis, which exhibits gradual and monotonic increase in overall performance with respect to the cycles since the inception of anomaly detection at the early stage.

Real-time Online Prediction of Data Driven Bearing Residual Life

In order to realize the predictive maintenance of key components under massive vibration data, real-time online prediction of the remaining life of different types of bearings, a data driven real-time online prediction method for bearing residual life is introduced. The method realizes the construction of the data-driven bearing residual life prediction model by selecting the bearing vibration data Spearman characteristic parameter selection, principal component analysis (PCA), health index fusion, and BP neural network fitting. The built model is continuously updated by real-time online acquisition of data to achieve real-time online prediction of bearing residual life. The accuracy and feasibility of the method for predicting the remaining life of different types of bearings are verified by experiments. Using this method to predict the remaining life of the bearing helps to achieve predictive maintenance of critical components, reduce unplanned downtime, increase production efficiency, and reduce production costs.

The cycloidal gearbox efficiency for different types of bearings—Sleeves vs. needle bearings

A comparative study of a cycloidal gearbox working with a different kind of bearings is presented in the article. Two kinds of the bearings used in the cycloidal gearbox are taken into consideration: sleeves and needle bearings. The theoretical efficiency calculation for both types of the bearing was compared with experimental results. The experiment was performed on a one-stage cycloidal gearbox designed, calculated, and manufactured during the research. The test was made under different working conditions, input speeds, and the braking torques. The results show an increase in the efficiency for the needle bearings compared to sleeves. The value of the efficiency is strictly connected with the braking torque of the gearbox as well as with the velocity. The article shows a very good correlation between calculated efficiency and the efficiency determined during the tests.

Fault diagnosis of rolling element bearings using fractional linear prediction and AI techniques

Rolling element bearings are one of the widely used and most critical components a rotating machinery. The performance of bearings is utmost important in applications such as power plants, automobiles, turbines, aerospace, materials handling and many more. In this paper, a new technique, fractional linear prediction, is presented for the fault diagnosis of bearings. For the examination of the proposed methodology, the vibration data of two different types of bearings are selected and analyzed. Three artificial intelligence techniques—rotation forest, support vector machine and artificial neural network—are used for the investigations. Comparison is also carried out among the artificial intelligence techniques to show their effectiveness towards fault diagnosis. Results indicate the superiority of rotation forest over support vector machine and artificial neural network.

Analysis of electrical motor mechanical failures due to bearings

Regardless of the high operational reliability of modern ball and roller bearings and advanced methods for their selection and durability calculation, a significant proportion of the failures in the electric motors, about 51% [1], are of mechanical nature. Often, mechanical damage leads to irreversible engine states and costly replacement. Careful theoretical analysis of bearing failures may indicate the root causes of their defects mainly from external for the bearings surface factors and to recommend proper preventive actions. Damage cause can undoubtedly refer to the way in which significant mechanical energy generated in the rotor is transmitted, most often through belt drives or elastic couplings, while the bearings themselves withstand the significant reaction forces that have occurred. A model analysis will be performed to reveal the interconnection of the mechanical power output of an electric motor via belt drive or an elastic coupling to the internal loads and friction of supporting ball or roller bearings. In addition, a suitable lubrication regime for bearings will be examined according to different types of bearings, shaft speed and temperatures.

A System for Clearance Measurement of Bearings before and after Assembling

In this work, a method for measuring the radial clearance of bearings mounted on a shaft before and after assembling in an automatically and standardized way will be described. A setup was constructed, which enables to measure the radial clearance of different types of bearings with different dimensions. Tests with roller and ball bearings with tapered bore and adapter sleeves show that the developed system provides as good or better results than the manual test method. The results are reliable, objective, independent from the user and reproducible. Based on the results, the process of assembling could be readjusted. By using a closed loop method, it will be achieved that bearings will be mounted on a shaft with the specified radial clearance which is defined by the manufacturer of the bearing.

Determination of bearing type effect on elastomeric bearing selection with SREI-CAD

The aim of this paper is to develop software for designing of steel reinforced elastomeric isolator (SREI) according to American Association for State Highway and Transportation Officials Load and Resistance Factor Design (AASHTO LRFD) Specifications. SREI is used for almost all bridge types and special structures. SREI-structures interface defines support boundary conditions and may affect the seismic performance of bridges. Seismic performance of the bridge is also affected by geometrical and materials properties of SREI. The selection of SREI is complicated process includes satisfying all the design constraints arising from code provisions and maximizing performance at the lowest possible cost. In this paper, design stage of SREI is described up to AASHTO LRFD 2012. Up to AASHTO LRFD 2012 analysis and design program of SREI performed different geometrical and material properties are created with C# object-oriented language. SREI-CAD, name of the created software, allows an accurate design for economical estimation of a SREI in a short time. To determine types of SREI effects, two different types of bearings, rectangular and circular with similar materials and dimension properties are selected as an application. Designs of these SREIs are completed with SREI-CAD. It is seen that ensuring the stability of circular elastomer bearing at the service limit state is generally complicated than rectangular bearing.

Do different types of bearings and noise from total hip arthroplasty influence hip-related pain, function, and quality of life postoperatively?

Background and purpose — Patient-reported outcome (PRO) is recognized as an important tool for evaluating the outcome and satisfaction after total hip arthroplasty (THA). We wanted to compare patient-reported outcome measure (PROM) scores from patients with ceramic-on-ceramic (CoC) THAs and those with metal-on-metal (MoM) THAs to scores from patients with metal-on-polyethylene (MoP) THAs, and to determine the influence of THA-related noise on PROM scores.Patients and methods — We conducted a nationwide cross-sectional questionnaire survey in a cohort of patients identified from the Danish Hip Arthroplasty Registry. The PROMs included were: hip dysfunction and osteoarthritis and outcome score (HOOS), EQ-5D-3L, EQ VAS, UCLA activity score, and questions about noise from the THA. The response rate was 85% and the number of responders was 3,089. Of these, 45% had CoC THAs, 17% had MoM THAs, and 38% had MoP THAs, with a mean length of follow-up of 7, 5, and 7 years, respectively.Results — Compared to MoP THAs, the mean PROM scores for CoC and MoM THAs were similar, except that CoC THAs had a lower mean score for HOOS Symptoms than did MoP THA. 27% of patients with CoC THAs, 29% with MoM THAs, and 12% with MoP THAs reported noise from their hip. For the 3 types of bearings, PROM scores from patients with a noisy THA were statistically significantly worse than those from patients with a silent MoP THA. The exception was noisy CoC and MoM THAs, which had the same mean UCLA activity score as silent MoP THAs.Interpretation — A high proportion of patients reported noise from the THA, and these patients had worse PROM scores than patients with silent MoP THAs.

Automatic Assembling of Bearings Including Clearance Measurement

The aim of the work is to develop a method for measuring the radial clearance of mounted antifriction bearings in an automatically and standardized way. The measurement of the radial clearance should take place on bearings mounted on a shaft before and after assembling.Based on this method a measurement setup was constructed which enables to measure the radial clearance of different types of bearings with different dimensions. First tests were done with roller bearings as well as ball bearings with different dimensions. Results of those tests show, that the developed system provides as good or better results than the manual test method for measuring the radial clearance of a bearing by hand. The measuring process itself runs automatically. The results are reliable, independent form the user and reproducible.Furthermore the whole assembling of the bearings on a shaft should be automated without to interrupt the process of mounting in order to measure the radial clearance. Based on the results of the measurements, the process of assembling could be readjusted. By using this closed loop method, it will be achieved that bearings will be mounted on a shaft with the specified radial clearance which is defined by the technical designer.A further part of this work is the automated documentation of the parameters which are connected with the process of assembling. During the assembling all important parameters will be measured automatically. In parallel they will be written to a database. Afterwards a protocol will be generated automatically, which could also be used as dimensional data sheet for each individual assembled bearing. A perfect traceability is than possible.