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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]

Condition monitoring of machine elements becomes more important over the last years. Different approaches to detect failures in mechanical components have been developed. All thesemethods are located at a distance from the point of interest, the observed machine element. This leads to uncertainties in the data, which influences the data quality negatively. Using the electrical impedance for conditionmonitoring enables in situ measurement with reduced uncertainties and higher data quality. In the last years, research considering this topicwas done, but a systematic overview is missing. In this article, a systematic literature research according to the PRISMA approach is fulfilled. The main questions are, what application fields for electrical impedance-based conditionmonitoring approaches exists andwhich research gaps are not addressed yet. At the end, 21 articles are categorized in their application fields. Analyzing their content, research questions are identified which have to be addressed in further investigations. [ABSTRACT FROM AUTHOR]

The measurement of the longitudinal rail profile is relevant to the condition monitoring of the rail infrastructure. The running surface is recognizable as a shiny metallic area on top of the rail head. The detection of the running surface is crucial for vehicle-based rail profile measurements, as well as for defect detection. This paper presents a methodology for the automatic detection of the running surface based on a laser profilometer. The detection of the running surface is performed based on the light reflected from the rail surface. Three rail surfaces with different surface conditions are considered. Supervised machine learning is applied to classify individual surface elements as part of the running surface. Detection by a linear support vector machine is performed with accuracy of > 90 % . The lateral position of the running surface and its width are calculated. The average deviation from the labeled widths varies between − 1.2 m m and 5.6 m m. The proposed measurement approach could be installed on a train for the future onboard monitoring of the rail network. [ABSTRACT FROM AUTHOR]

Rolling noise is a significant contributor to railway noise. Wheel and rail roughness are decisive for the emitted noise level. An optical measurement method installed on a moving train is suitable for closer monitoring of the rail surface condition. A measurement setup based on the chord method requires the sensors to be positioned in a straight line along the direction of measurement and in a stable lateral position. Measurements should always be performed within the shiny and uncorroded running surface, even when there are lateral movements of the train. In this study, concepts for the detection of the running surface and the compensation of lateral movements are investigated in a laboratory setting. The setup consists of a vertical lathe with a ring-shaped workpiece that incorporates an implemented artificial running surface. The detection of the running surface based on laser triangulation sensors and a laser profilometer is investigated. It is shown that the running surface can be detected using a laser profilometer that measures the intensity of the reflected laser light. It is possible to detect the lateral position and the width of the running surface. A linear positioning system is proposed to adjust the lateral position of the sensors based on the running surface detection of the laser profilometer. When the lateral position of the measuring sensor is disturbed by a movement with a wavelength of 18.85   m , the linear positioning system can keep the laser triangulation sensor inside the running surface for 98.44 % of the measured data points at a velocity of approximately 7.5   km   h − 1 . The mean positioning error is 1.40   mm . By implementing the proposed system on the train, future studies can be conducted to examine the lateral position of the running surface as a function of the various operational parameters of the train. [ABSTRACT FROM AUTHOR]

In this study, we developed the electrical impedance method which simultaneously measures the thickness and breakdown ratio of oil films in elastohydrodynamic (EHD) line contacts within thrust needle roller bearings. Initially, we theoretically demonstrated that the oil film thickness and breakdown ratio can be simultaneously measured using the complex impedance that is produced when an AC voltage is applied to EHD line contacts. To verify the measurement accuracy of the electrical method, we monitored the oil film thickness of a thrust needle roller bearing and compared it with the theoretical value. The results revealed that the oil film thickness was thinner than the theoretical value immediately after starting the test, with the breakdown ratio being greater than 0 (indicating mixed lubrication); however, the breakdown ratio decreased over time, and the oil film thickness nearly matched the theoretical value one hour after starting the test, when it is believed that running-in wear is complete (i.e., breakdown ratio ≈ 0). Furthermore, following the test, after examining the race surface, we confirmed that running-in wear had indeed occurred. These results suggest that the developed method can monitor the lubrication conditions in EHD line contacts, such as those in thrust needle roller bearings, in detail. [ABSTRACT FROM AUTHOR]

Rail corrugation is a degradation phenomenon that manifests as a quasi-periodic irregularity on the running surface of the rail. It is a critical problem for urban railway lines because it induces ground-borne vibrations transmitted to the buildings near the infrastructure, causing complaints from the inhabitants. A typical treatment to mitigate the rail corrugation problem is the periodic grinding of the rails, performed by dedicated vehicles. The scheduling of rail maintenance is particularly critical because it can be performed only when the service is interrupted. A procedure for the continuous monitoring of rail corrugation is proposed, based on axlebox acceleration measurements. The rail irregularity is estimated from the measured acceleration by means of a frequency domain model of vertical dynamics of the wheel–rail interaction. The results obtained by using two different methods (a state-of-the-art method and a new one) are compared. Finally, the study of the evolution of the power content of the rail irregularity enables the identification of the track sections where corrugation is developing and rail grinding is necessary. [ABSTRACT FROM AUTHOR]

This paper presents a review-based comparative study of state-of-the-art technologies, technical challenges and research barriers, and development trends of gearboxes used in tidal current energy converters (TCECs). Currently, the development of commercial projects using TCECs is still in the demonstration phase. While many drivetrain designs and configurations of TCECs inherit from those of wind turbines, different operational constraints, e.g., high-torque and low-speed conditions, make TCECs potentially suffer from high failure rates in harsh deep-sea environments. Evidence of these potentially high failure rates highlights the need for adopting the most resilient drivetrain options with a high degree of maintainability. The gearbox option is a critical issue that needs to be addressed for the choice of the drivetrain configuration due to its longest downtime per failure among all drivetrain components of TCECs. The main purpose of this study is to review current gearbox technologies of TCECs with advantages and disadvantages as well as to identify future technical challenges and research barriers. Gearbox maintenance is also a focal point in this study. We present a discussion of the operation phase to highlight operational maintenance methods currently used in the tidal energy industry. This study will, therefore, address the critical issue by proposing a review-based gearbox option comparison and discussing potential solutions to reduce operation and maintenance costs of gearboxes of TCECs. [ABSTRACT FROM AUTHOR]

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.

Rolling bearings face different damaging effects: Besides mechanical effects, current-induced bearing damage occurs in electrical drive systems. Therefore, it is of increasing interest to understand the differences leading to known electrical damage patterns. It is of utmost importance not to consider the harmful current passage in the machine element as an isolated phenomenon but to take into account the whole drive system consisting of the machine elements, the electric motor and the connected power electronics. This publication works toward providing an overview of the state-of-the-art of research regarding electrical bearing currents. [ABSTRACT FROM AUTHOR]

Condition monitoring enables transparency, as for example irregularities are detected automatically and reported. A condition forecast, however, requires more. In contrast to AI black box methods, frequently used in this context, a combination of existing expert knowledge and classical statistics is used as a method for a reliable determination of the remaining component‐lifetime. This works, if meaningful historical data are available in a sufficient quantity and quality. And this in turn requires a corresponding number of machines that are as identical in construction as possible and which must also be subject to a defined test regime, temporally closely monitored outside the production process. However, the quantity can be significantly smaller than the number of cases required for a prescient analysis of the correlations between a condition parameter and the wear condition of a specific component. The main target audience of the strategy presented here is therefore in particular manufacturers of series machines who wish to offer maintenance packages with corresponding availability guarantees and on‐site support for maintenance personnel. [ABSTRACT FROM AUTHOR]

Rolling bearings are frequently subjected to high stresses within modern machines. To prevent bearing failures, the topics of condition monitoring and predictive maintenance have become increasingly relevant. In order to efficiently and reliably maintain rolling bearings in a predictive manner, an estimate of the remaining useful life (RUL) is of great interest. The RUL prediction quality achieved when using machine learning depends not only on the selection of the sensor data used for condition monitoring, but also on its preprocessing. In particular, the execution of so-called feature engineering has a major impact on prediction quality. Therefore, in this paper, various methods of feature engineering are presented based on rolling–bearing endurance tests and recorded structure-borne sound signals. The performance of these methods is evaluated in the context of a regression-based RUL model. Furthermore, the way in which the quality of RUL prediction can be significantly improved is demonstrated, by adding further processed, time-considering features. [ABSTRACT FROM AUTHOR]

Functional surfaces in relative contact and motion are prone to wear and tear, resulting in loss of efficiency and performance of the workpieces/machines. Wear occurs in the form of adhesion, abrasion, scuffing, galling, and scoring between contacts. However, the rate of the wear phenomenon depends primarily on the physical properties and the surrounding environment. Monitoring the integrity of surfaces by offline inspections leads to significant wasted machine time. A potential alternate option to offline inspection currently practiced in industries is the analysis of sensors signatures capable of capturing the wear state and correlating it with the wear phenomenon, followed by in situ classification using a state-of-the-art machine learning (ML) algorithm. Though this technique is better than offline inspection, it possesses inherent disadvantages for training the ML models. Ideally, supervised training of ML models requires the datasets considered for the classification to be of equal weightage to avoid biasing. The collection of such a dataset is very cumbersome and expensive in practice, as in real industrial applications, the malfunction period is minimal compared to normal operation. Furthermore, classification models would not classify new wear phenomena from the normal regime if they are unfamiliar. As a promising alternative, in this work, we propose a methodology able to differentiate the abnormal regimes, i.e., wear phenomenon regimes, from the normal regime. This is carried out by familiarizing the ML algorithms only with the distribution of the acoustic emission (AE) signals captured using a microphone related to the normal regime. As a result, the ML algorithms would be able to detect whether some overlaps exist with the learnt distributions when a new, unseen signal arrives. To achieve this goal, a generative convolutional neural network (CNN) architecture based on variational auto encoder (VAE) is built and trained. During the validation procedure of the proposed CNN architectures, we were capable of identifying acoustics signals corresponding to the normal and abnormal wear regime with an accuracy of 97% and 80%. Hence, our approach shows very promising results for in situ and real-time condition monitoring or even wear prediction in tribological applications. [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.

A complete surveillance strategy for wind turbines requires both the condition monitoring (CM) of their mechanical components and the structural health monitoring (SHM) of their load-bearing structural elements (foundations, tower, and blades). Therefore, it spans both the civil and mechanical engineering fields. Several traditional and advanced non-destructive techniques (NDTs) have been proposed for both areas of application throughout the last years. These include visual inspection (VI), acoustic emissions (AEs), ultrasonic testing (UT), infrared thermography (IRT), radiographic testing (RT), electromagnetic testing (ET), oil monitoring, and many other methods. These NDTs can be performed by human personnel, robots, or unmanned aerial vehicles (UAVs); they can also be applied both for isolated wind turbines or systematically for whole onshore or offshore wind farms. These non-destructive approaches have been extensively reviewed here; more than 300 scientific articles, technical reports, and other documents are included in this review, encompassing all the main aspects of these survey strategies. Particular attention was dedicated to the latest developments in the last two decades (2000–2021). Highly influential research works, which received major attention from the scientific community, are highlighted and commented upon. Furthermore, for each strategy, a selection of relevant applications is reported by way of example, including newer and less developed strategies as well. [ABSTRACT FROM AUTHOR]

This paper presents methods for continuous condition monitoring of railway switches and crossings (S&C, turnout) via sleeper-mounted accelerometers at the crossing transition. The methods are developed from concurrently measured sleeper accelerations and scanned crossing geometries from six in situ crossing panels. These measurements combined with a multi-body simulation (MBS) model with a structural track model and implemented scanned crossing geometries are used to derive the link between the crossing geometry condition and the resulting track excitation. From this analysis, a crossing condition indicator C λ 1 − λ 2 ,   γ is proposed. The indicator is defined as the root mean square (RMS) of a track response signal γ that has been band-passed between frequencies corresponding to track deformation wavelength bounds of λ 1 and λ 2 for the vehicle passing speed (f = v/   λ ). In this way, the indicator ignores the quasi-static track response with wavelengths predominantly above λ 1 and targets the dynamic track response caused by the kinematic wheel-crossing interaction governed by the crossing geometry. For the studied crossing panels, the indicator C 1 − 0.2   m ,   γ ( λ 1 = 1 and λ 2 = 0.2 ) was evaluated for γ = u, v, or a as in displacements, velocities, and accelerations, respectively. It is shown that this condition indicator has a strong correlation with vertical wheel–rail contact forces that is sustained for various track conditions. Further, model calibrations were performed to measured sleeper displacements for the six investigated crossing panels. The calibrated models show (1) a good agreement between measured and simulated sleeper displacements for the lower frequency quasi-static track response and (2) improved agreement for the dynamic track response at higher frequencies. The calibration also improved the agreement between measurements and simulation for the crossing condition indicator demonstrating the value of model calibration for condition monitoring purposes. [ABSTRACT FROM AUTHOR]