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This work presents an investigation on the influence of pitting in EHL rolling contact. The pitting geometry comes as an extension on the architecture for multi-physical numerical calculations of rolling element bearing contacts based on OpenFOAM. The model utilized is built according to the state-of-the-art for fluid–solid interaction and electro-quasi-static fields. In this framework, the contact is reduced to be two dimensional in order to reduce the computing costs needed. The changes in the electric properties, which are caused by pittings, are measured using the electric capacitance of the contact and put in perspective with regard to the EHL contacts geometry. The investigation delivers an evaluation on how surface degradation, in both the pitting width w and pitting depth d of the contact, affects the electric properties. It can be seen that the maximum deviations with different reduced radii for the same pitting structures are around 5% and would therefore hardly be distinguishable with corresponding measurements. By comparing the same data for the same ratio of pitting width to reduced radius, on the other hand, differences of up to 50% can be seen. [ABSTRACT FROM AUTHOR]

The present work investigates the technical feasibility of a condition monitoring setup aiming at the detection of gauge corner cracks (aka head checks) in pearlitic railway rails, using a wayside (i.e., stationary) setup with surface acoustic waves (SAW) as its detection principle. The experimental SAW setup consists of a pitch-catch setup using piezo transducers equipped with comb adaptors to excite and measure narrowband Rayleigh waves with a center frequency of 1 MHz. SAW experiments were performed on a rail subjected to cyclic loading in a 1:1 wheel–rail test rig yielding the specific rolling contact fatigue, i.e., head checks. Elastodynamic finite integration technique (EFIT) simulations were performed to analyze the surface and bulk wave propagation in the rail and to predict the signals at specific receiver positions. SAW transmission and reflection scenarios at cracks were analyzed numerically via modelled variations of gauge corner crack configurations according to number of cracks (0–3) and depth (0, 0.5 mm and 1 mm). The numerical and the experimental results each show a clear correlation between the appearance and intensity of head check damage and the wave attenuation in transmission mode. [ABSTRACT FROM AUTHOR]

Rolling element bearings (REBs) are vital parts of rotating machinery across various industries. For preventing breakdowns and damages during operation, it is crucial to establish appropriate techniques for condition monitoring and fault diagnostics of these bearings. The development of machine learning (ML) brings a new way of diagnosing the fault of rolling element bearings. In the current work, ML models, namely, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN), are used to classify the faults associated with different ball bearing elements. Using open-source Case Western Reserve University (CWRU) bearing data, machine learning classifiers are trained with extracted time-domain and frequency-domain features. The results show that frequency-domain features are more convincing for the training of ML models, and the KNN classifier has a high level of accuracy compared to SVM. [ABSTRACT FROM AUTHOR]

Rolling element bearings are widely employed in rotating machines. The health monitoring and the residual life prediction of bearings are significant to the reliable operation of the equipment. This paper deals with the principle of a time-domain-based residual life prediction method for rolling element bearings. In particular, a quantitative evaluation of bearing degradation is performed by evaluating the sensitivity of the 13 time-domain features. Meanwhile, the optimal feature set is generated through a sensitivity evaluation analysis approach. Next, the max-min normalization and the multiple linear regression (MLR) methods are applied to construct a healthy index. Based on the healthy index, the degradation trajectory is obtained by using the locally weighted scatterplot smoothing (LOWESS) method. Two data sets generated by the outer race fault are used to verify the effectiveness of the proposed method. The experimental results show that the proposed method can realize the residual life prediction of rolling element bearings effectively. [ABSTRACT FROM AUTHOR]

The Performance of rolling element bearings has a significant influence on reliability and safety in the various engineering fields. While heading toward the condition evaluation of rolling element bearings, the higher dimensionality of feature space became a crucial concern. Thus, to have a check on the hurdles, this paper proposes a novel approach of bearing condition assessment utilizing Local mean decomposition (LMD) and spectral clustering (SC). Spectral Clustering (SC) is an influential tool, which heals the curse of dimensionality and facilitates the model by imparting a systematized structure. SC focuses more on connectivity rather than geometrical vicinity; moreover, the provision of a similarity matrix makes SC more reliable and increases its performance. To enrich the efficiency of SC, a novel decision criterion, PI (Parting Index) aiming the optimal number of clusters based on a new similarity indicator and disorder indicator coined as Synergic Association Index (SAI) and Separation Index (SI) respectively, has been proposed. The work aims to achieve better performance degradation assessment (PDA) by the application of the new SC-PI approach with the following steps; the first step is the decomposition of vibration signal into product functions (PFs) utilizing LMD; the second step involves the extraction of proper fault features; third, the extracted features are classified with the SC-PI method; finally the feature or attribute vectors are endowed to trained model and confidence value (CV) is calculated. The proposed method is validated on two datasets of different fault types. The results so obtained indicates that the SC-PI method easily differentiates the various stages when compared to different assessment techniques i.e. time-domain features, self-organizing map (SOM), k-medoids and Gaussian mixture model (GMM). • A novel method for performance degradation assessment is proposed. • LMD and SVD are employed to extract fault features. • An advancement to Spectral clustering with a novel method for the optimal number of clusters has been made to prepare an assessment modal and index. [ABSTRACT FROM AUTHOR]

A rolling bearing is an essential component of a rotating mechanical transmission system. Its performance and quality directly affects the life and reliability of machinery. Bearings’ performance and reliability need high requirements because of a more complex and poor working conditions of bearings. A bearing with high reliability reduces equipment operation accidents and equipment maintenance costs and achieves condition-based maintenance. First in this paper, the development of technology of the main individual physical condition monitoring and fault diagnosis of rolling bearings are introduced, then the fault diagnosis technology of multi-sensors information fusion is introduced, and finally, the advantages, disadvantages, and trends developed in the future of the detection main individual physics technology and multi-sensors information fusion technology are summarized. This paper is expected to provide the necessary basis for the follow-up study of the fault diagnosis of rolling bearings and a foundational knowledge for researchers about rolling bearings. [ABSTRACT FROM AUTHOR]

The rolling element bearing is one of the most critical components that determine the machinery health and its remaining lifetime in modern production machinery. Robust Predictive Health Monitoring tools are needed to guarantee the healthy state of rolling element bearing s during the operation. A Predictive Health Monitoring tool indicates the upcoming failures which provide sufficient lead time for maintenance planning. The Predictive Health Monitoring tool aims to monitor the deterioration i.e. wear evolution rather than just detecting the defects. The Predictive Health Monitoring procedures contain detection, diagnosis and prognosis analysis, which are required to extract the features related to the faulty rolling element bearing and estimate the remaining useful lifetime. The purpose of this study is to review the Predictive Health Monitoring methods and explore their capabilities, advantages and disadvantage in monitoring rolling element bearings. Therefore, the study provides a critical review of the Predictive Health Monitoring methods of the entire defect evolution process i.e. over the whole lifetime and suggests enhancements for rolling element bearing monitoring. [ABSTRACT FROM AUTHOR]

Rolling contact wear is a complex phenomenon that might involve different wear mechanisms (adhesive, abrasive, fatigue and corrosive) and different stress concentration mechanisms (asperity, dent, debris, inclusions, etc.). The interactions among these mechanisms might accelerate or decelerate the overall wear progress. Therefore, it is complicated to model and monitor the fluctuations of wear progress. The current descriptive models are either describing individual physical phenomena within rolling contact wear or describing a specific stage of wear progress. Thus, the interactions among different wear mechanisms and the transition events among different stages of wear progress are not sufficiently addressed. Therefore, the purpose of this paper is to propose a descriptive model of the wear evolution process in rolling bearings over the whole lifetime. The descriptive model utilises a wide range of empirical findings in the literature to describe the wear interactions and evolution in the five-stage scenario: running-in, steady-state, defect initiation, defect propagation, and damage growth. The new descriptive model provides the most probable scenario of wear evolution in rolling bearings, which is useful for modelling and monitoring the wear progress. It illustrates the wear evolution stages, the involved wear mechanisms in each stage, the interaction among wear mechanisms in each stage, the surface topology changes and the influencing factors within each stage. For design, condition monitoring and prognosis purposes, these aspects are significantly important to understand, model, test and monitor the wear evolution process. [ABSTRACT FROM AUTHOR]

Abstract: The usage of polymers in bearings has been increasing in recent years due to certain valuable tribological properties, including their ability to self-lubricate and their resistance to corrosion. These advantages encourage manufacturers to use polymer bearings in corrosive and lubricant-free work environments as well as applications in which weight reduction is desirable. The objective of this study is to compare the performance of polymer with metal in rolling contact. This is carried out by monitoring the acoustic emission, vibration and operating speed of metal and polymer bearings during their operation. A set of tests was conducted on 8mm radial bearings comprising polymeric inner race, outer race, and cage as well as 52mm thrust bearings comprising polymeric top race, bottom race and retainer. These bearing components were manufactured using two different polymers: polyetheretherketone (PEEK) and polytetrafluoroethylene (PTFE). The rolling elements used in these bearings were made of alumina. The performance of these materials in both radial and thrust bearings was evaluated. A second set of tests was conducted to compare the performance of these polymers with stainless steel in similar bearings. 8mm polymer ball bearings and 8mm metal bearings under dry lubrication conditions were used in these tests. Performance is compared using vibration, acoustic emission as well as changes in the rotational speed. A lower level of vibration is observed with the usage of PEEK in the bearing elements when compared to that of PTFE and metal. However the transfer film formation in PEEK bearings resulted in unstable operating speed of the bearing. Stable operating speeds after an initial running-in period is observed due to transfer film formed in carbon-filled PTFE bearings. However a higher level of vibration is observed in this material when compared to that of the PEEK bearings. [Copyright &y& Elsevier]

By using non-contact gap sensors equipped on non-rotating parts of a bogie, a new measuring method of wheel/rail contact forces has been developed. The developed system has been verified to have sufficient durability for Continuous measurement on in-service trains and sufficient practical accuracy after various stand tests and train running tests. After a long-period of continuous measurement on a commercial subway line, some important characteristics of wheel/rail contact mechanics were found by the analysis of measured data. Numerical simulations of curving with a full vehicle model using multi-body dynamics software were carried Out, and according to the comparison with measured data, simulation results agree well with measured data in the steady-state values of derailment coefficients considering friction coefficient. [ABSTRACT FROM AUTHOR]

Exploring an effective assessment index is significant for performance degradation assessment, which has been proposed to realize equipments’ near-zero downtime and maximum productivity. In this paper, the Kolmogorov-Smirnov test based on an autoregressive model is proposed to assess the performance degradation of rolling bearings. Accelerated life test (in Hangzhou Bearing Test and Research Center) of rolling bearings was performed to collect vibration data over a whole lifetime (normal-fault-failure). The result shows that the Kolmogorov-Smirnov test method can obviously detect incipient weak defects and can reflect performance degradation process well. In particular, it can detect abnormal stages earlier before the bearing steps into failure, which is significant in condition maintenance and prognosis. [ABSTRACT FROM PUBLISHER]

Prediction of the vibration response due to defects on rolling element bearings requires having an accurate representative vibration model. In this paper, a new model for vibration generation in the rolling element bearings has been introduced. The proposed model assumes a stochastic source of vibration excitation, which is produced as a result of metallic contact between bearing elements during rolling. This model explains high frequency vibration in the acceleration spectrum clearly. When a defect grows in the bearing, the roughness of the contacting surfaces increases locally and stochastic excitation becomes stronger in the defective area. The increased vibration level at the defective area is a good indicator of bearing faults. A numerical simulation of the proposed model was validated with experimental results. [ABSTRACT FROM AUTHOR]

This paper presents a novel time–frequency-based feature recognition system for gear fault diagnosis using autocorrelation of continuous wavelet coefficients (CWC). Furthermore, it introduces an original mathematical approximation of gearbox vibration signals which approximates sinusoidal components of noisy vibration signals generated from gearboxes, including incipient and serious gear failures using autocorrelation of CWC. First, the drawbacks of the continuous wavelet transform (CWT) have been eliminated using autocorrelation function. Secondly, the autocorrelation of CWC is introduced as an original pattern for fault identification in machine condition monitoring. Thirdly, a sinusoidal summation function consisting of eight terms was used to approximate the periodic waveforms generated by autocorrelation of CWC for normal gearboxes (NGs) as well as occurrences of incipient and severe gear fault (e.g. slight-worn, medium-worn, and broken-tooth gears). In other words, the size of vibration signals can be reduced with minimal loss of significant frequency content by means of the sinusoidal approximation of generated autocorrelation waveforms of CWC as reported in this paper. [Copyright &y& Elsevier]

Abstract: The acoustic emission (AE) technique was applied to rolling contact fatigue tests using a test-rig running under constant load and speed for detecting the incipient damage and damage location. This incipiently-damaged roller was investigated in detail and monitored by further running to determine the damage severity and to understand the surface damage propagation process by applying the AE techniques. The conventional AE parameters and AE signal features were studied, and their relation with the AE source locator hit count rate were correlated. The results demonstrated the successful use of the AE measurement unit, which is principally, consists of the AE data analyzer and the AE source locator as a new system for detecting incipient damage produced by fatigue. Moreover, the system is able to forecast the position of the damage in the roller, capable of providing an indication of the severity of damage i.e. damage size, and thus it could allow the user to monitor the rate of further degradation of the rolling elements. [Copyright &y& Elsevier]

Rolling element bearings are one of the vital components of any rotating machinery. The failure of the bearings will lead to malfunction or complete shut down of the machinery. This results in substantial time and economic losses. It is therefore, important to know the health of the bearings and the severity of the defect before the catastrophic consequences. This paper describes the suitability of the statistical parameters such as kurtosis and crest factor and the spectrum analysis as the technique for monitoring defect. The paper also describes the effect of the speed and the defect size on these parameter. [ABSTRACT FROM AUTHOR]

Abstract: Condition monitoring of gears with vibration analysis is well established whilst the application of acoustic emission (AE) to gear defect diagnosis and monitoring is still in its infancy. This paper details results of an experimental programme to ascertain and validate the applicability of AE to seeded gear defect identification. Furthermore, comparisons are made to vibration diagnosis. As a direct consequence of the experimental programme, the relationship between temperature, oil film thickness and AE activity were investigated. It is shown that similar to the lubricant film thickness between non-conforming surfaces under isothermal conditions, AE activity is not influenced by load. Limitations of applying AE to seeded defect identification are presented and it is concluded that the source of AE activity is attributed to asperity contact. [Copyright &y& Elsevier]

Bearings are essential components in rotating machines. They ensure the rotation and power transmission. So, these components are essential elements for industrial machines. Thus, real-time monitoring is required to detect a possible anomaly, diagnose the failure of rolling bearing and follow its evolution. This paper presents a methodology for automatic online implementation of fault diagnosis of rolling bearings, by AOC-OPTICS (automatic online classification monitoring based on ordering points to identify clustering structure, OPTICS). The algorithm consists of three phases namely: initialization, detection and follow-up. These phases use the combination of features extraction methods, smart ranking, features weighting and classification by the OPTICS method. Two methods have been integrated in the dimension reduction step to improve the efficiency of detection and the followed of the defect (relief method and t-distributed stochastic neighbor embedding method). Thus, the determination of the internal parameters of the OPTICS method is improved. A regression model and exponential model are used to track the fault. The analytical simulations discuss the influence of parameters automation. Experimental validation shows detection with 100% accuracy and regression models of monitoring reaching R 2 = 0.992 . [ABSTRACT FROM AUTHOR]

To estimate the safety against flange-climb derailments the safety measures based on Nadal's formula have been used in the world. These measures are effective, but the safety margin against flange-climb cannot be estimated quantitively because the actual friction condition between rail and wheel flange cannot be measured by conventional methods. In this paper, the authors propose a new method for the estimation of the friction condition between rail and wheel flange, and using the estimated value of the friction coefficients, a safety measure, the "flange-climb index" (FCI), is proposed. The value of FCI can evaluate the margin for the safety against flange-climb occurrences quantitatively according to the friction conditions. • Y/Q in Nadal's formula cannot examine the margin against flange-climb occurrence quantitatively. • The authors propose a new safety measure index FCI against flange-climb derailments. • FCI can quantitatively estimate the margin against flange-climb occurrences. • FCI is calculated considering the friction coefficient between wheel flange and rail. • The friction coefficient can be estimated by look-up tables created by MBD simulations. [ABSTRACT FROM AUTHOR]