Wyniki wyszukiwania - BazTech

1

Exploration of frequency characteristics of ball bearing with uneven load bearing effect caused by wear failure of multiple rolling bodies under starved lubrication

Huo Zhongtang, Chen Jianqi, Hao Lingjuan, Gao Jiansong

Journal of Theoretical and Applied Mechanics

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2025

|

Vol. 63 nr 1

197--209

EN

Pointed at the problem of increased wear of rolling bodies (RBs) in a bearing under starved lubrication as well as abnormal vibration and increased noise of the bearing after occurence of wear fault, a fault dynamics model was proposed to simulate the interaction between components by quantifying the degree of rolling bodies wear. The results indicated that the bearing exhibited an uneven load bearing effect after the wear fault. The fractional multiple of the rotation frequency of the cage could be used as the basis for monitoring wear fault of rolling bodies. The research provides a reference for the diagnosis of wear fault in rolling bodies.

2

Spectral feature - based neural classification for efficient bearing aging assessment in electric motors

Badeli Mina G. Z., Kara Duygu B.

Diagnostyka

|

2025

|

Vol. 26, No. 2

art. no. 2025212

EN

This study proposes a novel methodology for classifying bearing aging stages in induction motors by leveraging a compact and effective set of spectral features. Two advanced neural network classifiers - a Pattern Recognition Neural Network (PRNN) trained with the Levenberg-Marquardt algorithm and a Feedforward Neural Network (FFNN) optimized with the Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm - were compared. Experimental results demonstrate the FFNN's superior accuracy and robustness in classifying eight distinct aging grades. The primary innovation of this study lies in the use of five key spectral features extracted from the critical 2-4 kHz frequency band. This feature set significantly reduces dimensionality while preserving the descriptive features needed to characterize the aging process, enabling efficient and precise diagnostics. By employing this approach, the methodology not only enhances computational efficiency but also facilitates seamless integration into real-world fault detection and maintenance systems. Beyond fault detection, this work establishes a foundation for accurately determining bearing aging stages, creating opportunities to estimate bearing lifespan more precisely. By providing actionable insights into the aging process, it enables proactive maintenance strategies that reduce downtime and operational costs while enhancing machinery reliability. Future applications may extend this methodology to broader predictive maintenance frameworks and condition assessment tasks across various industrial domains.

3

Rotating machinery reliability assessment based on improved extreme learning machine and hippopotamus optimization algorithm

Isham Muhammad Firdaus Bin, Kamal Muhammad Harith Mohd, Raheimi Amirulaminnur, Saufi Mohd Syahril Ramadhan Mohd, Lim Meng Hee, Leong Muhd Salman, Waziralilah Nur Fathiah

Advances in Science and Technology. Research Journal

|

2025

|

Vol. 19, no. 2

82--94

EN

The utilisation of rotating machinery across diverse industrial applications underscores the critical importance of evaluating its reliability to ensure the safe operation of these systems. Bearings, as fundamental components within engineering facilities, hold particular significance; their malfunction can result in severe safety incidents, heightened maintenance expenditures, and considerable economic consequences. Extreme learning machine (ELM) represents a machine learning approach that proficiently addresses numerous challenges inherent in conventional machine learning algorithms. Nonetheless, the efficacy of the ELM may deteriorate and yield inaccurate results due to an inappropriate use of its parameters, which include input weights, biases, and the number of hidden neurons. This paper proposes an improved ELM (IELM) model that incorporates the Hippopotamus optimization algorithm (HOA) to optimise the parameters and enhance the performance of the ELM in rotating machinery reliability assessment. Initially, the HOA method is employed to identify optimised parameter values for the ELM. Subsequently, these optimised values are integrated into the proposed IELM-HOA framework for the purpose of fault classification. This study utilises time-domain statistical features to extract significant information from the vibration signals. The dataset comprises vibration signals derived from both online bearing datasets and experimental bearing datasets. The findings indicate that the proposed IELM-HOA method enhances the performance of the ELM technique. Furthermore, it demonstrates the capability to exceed and compete with recently introduced fault diagnosis methodologies

4

Bearing fault diagnosis based on cross-machine statistical features generalization and improved extreme learning machine

Kamal Muhammad Harith Mohd, Isham Muhammad Firdaus Bin, Raheimi Amirulaminnur, Saufi Mohd Syahril Ramadhan Mohd, Saad Wan Aliff Abdul

Advances in Science and Technology. Research Journal

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2025

|

Vol. 19, no. 5

407--421

EN

Asset reliability is among the primary objectives in technological advancements and effective maintenance is essential to guarantee optimal performance of machineries while upholding safety requirements. Intelligent models based on machine learning and deep learning techniques have been extensively suggested for advanced maintenance procedures. In recent times, there has been a trend in fault diagnosis studies towards cross-machine diagnosis which involves multiple machines. Therefore, this paper proposes a cross-machine bearing fault diagnosis trained without faulty data of target machine; based on selected generalized statistical vibration features and improved extreme learning machine. This work utilized an online bearing dataset from a source machine and experimental datasets from a target machine. The statistical vibration features were derived from both datasets (online and experimental) and subsequently chosen based on distinctive characteristics in features. Next, specific characteristics will be input into the improved extreme learning machine (ELM) technique for the purpose of fault categorization. The suggested model demonstrated substantial cross-machine classification ability, with an accuracy rate of up to 98.9%.

5

Application of dispersion entropy to journal bearing hydrodynamic stability

Blaut Jędrzej

Vibrations in Physical Systems

|

2024

|

Vol. 35, nr 2

art. no. 2024217

EN

The article consists of two parts: analytical and experimental. The first part discusses the theory of signal analysis in nonlinear systems, particularly in the context of sliding bearings. The second part describes an experiment where a mechanical system achieves stable operation. Three types of signals reflecting different states of bearing operation are identified: unstable, transient and stable operation. The study showed that the value of DisEn (indicator) is related to the operating state of the bearing. This allows easier diagnosis of the bearing's condition and suggests the possibility of dispensing with a single sensor. In addition, a decrease in the difference between DisEn values for the x and y axes was noted in unstable operation.

6

New Solutions for Lubricant Viscosity and Temperature Variations Across the Thin Film on Arbitrary Sliding Bearing Surfaces

Wierzcholski Krzysztof, Miszczak Andrzej

Tribologia

|

2024

|

nr 4

103--113

EN

The aim of this paper is to analyse the changes in apparent dynamic viscosity and temperature across any thin non-Newtonian lubricating liquid layer, and determine the influence of such variations on the hydrodynamic pressure and load-carrying capacity for arbitrary curvilinear monotone or non-monotone rotational and nonrotational sliding bearing surfaces. This requires determining particular semi-analytical solutions of a strongly non-linear, second-order partial differential system of five equations with variable coefficients in curvilinear coordinates, and imposing proper curvilinear boundary conditions on it. After initial numerical calculations for any bearing surface, especially with a conical or spherical shape, the changes in temperature and viscosity across the thickness of the lubricating film change the load-carrying capacity by nearly 20 per cent compared to the results obtained from classic calculations in the contemporary scientific literature, where the temperature and oil dynamic viscosity are assumed constant across the film thickness.

PL

Celem niniejszej pracy są zmiany lepkości pozornej i temperatury w poprzek cienkiej, ogólnie dowolnej nienewtonowskiej warstewki cieczy smarującej oraz wyznaczenie wpływu tych zmian na ciśnienie hydrodynamiczne i nośność dowolnych obrotowych i nieobrotowych, a także monotonicznych i niemonotonicznych powierzchni łożysk ślizgowych. Powyższy cel narzuca konieczność wyznaczenia semi-analitycznych rozwiązań silnie nieliniowego układu pięciu równań różniczkowych cząstkowych rzędu drugiego o zmiennych współczynnikach w układach krzywoliniowych wraz z nałożeniem na te rozwiązania krzywoliniowych warunków brzegowych. Po obliczeniach dla dowolnych powierzchni łożysk, szczególnie o kształtach stożkowych, sferycznych, parabolicznych, obserwujemy, że zmiany temperatury i lepkości w poprzek grubości cienkiego filmu smarującego powodują istotne, średnio 20-procentowe zmiany nośności łożyska w porównaniu z rezultatami uzyskanymi z obliczeń klasycznych zawartych we współczesnej literaturze naukowej dla stałej wartości temperatury i lepkości dynamicznej przyjętej po grubości filmu.

7

Method of Statistical Analysis of Frictional and Thermal Phenomena in Selected Friction Nodes

Lepiarczyk Dariusz

Tribologia

|

2024

|

nr 4

55--64

EN

The use of thermographic techniques or contact temperature measurements in diagnostics can be a rich source of information on the technical condition of the object examined. The application of diagnostics using thermographic techniques allows for the precise localization of malfunctioning machine components and enables effective repair without replacing properly functioning parts. This is particularly important in bearing elements, where relative rotational movement generates significant heat due to friction between cooperating components. This article presents the results of studies on selected friction joints working under various operating conditions. Based on the results obtained from the measurement and using statistical tools, a method was developed to evaluate the correct operation of the selected friction nodes The verification of the developed test method was carried out on the results obtained from thermal imaging measurements.

PL

Wykorzystanie w diagnostyce technik termowizyjnych lub pomiarów kontaktowych temperatury może być bogatym źródłem informacji o stanie technicznym badanego obiektu technicznego. Zastosowanie diagnostyki z wykorzystaniem technik termowizyjnych umożliwia precyzyjne zlokalizowanie wadliwie funkcjonujących elementów maszyn i urządzeń oraz dokonanie skutecznej naprawy bez niepotrzebnej wymiany prawidłowo działających części. Szczególnie ma to znaczenie w elementach łożyskowych, w których względny ruch obrotowy powoduje wydzielanie znacznej ilości ciepła w wyniku tarcia współpracujących elementów. W opracowaniu przedstawiono wyniki badań wykonane dla wybranych węzłów ciernych, pracujących w różnych warunkach eksploatacyjnych. Na podstawie otrzymanych wyników pomiarów z wykorzystaniem narzędzi statystycznych opracowano metodę oceny poprawnego stanu pracy wybranych węzłów tarcia. Weryfikację opracowanej metody badawczej przeprowadzono w oparciu o wyniki uzyskane z pomiarów termowizyjnych.

8

Bearing fault analysis utilizing fuzzy logic methodology for enhanced diagnostic accuracy

Santoso Santoso, Hartayu Ratna, Ridho'i Ahmad, Andriawan Aris Heri, Pambudi Wahyu Setyo, Anwar Ahmad Nuril, Muharom Syahri

Przegląd Elektrotechniczny

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2024

|

R. 100, nr 10

73--78

EN

This research aims to design a tool that can be used to detect damage or malfunctions in induction motors, especially in the bearing part which is the main driving component. Using the MPU 6050 accelerometer sensor and sound sensor module with the Arduino Nano microcontroller and the HC 05 Bluetooth module as a medium for sending and acquiring signal data. The signal data obtained in the form of a datalog with the extension .txt is then processed further with Matlab software to find out information on the characteristics of sound signals and vibration signals generated by induction motor bearings. Using a cut-off signal filter low pass filter for voice signal filter processing and fast Fourier transform (FFT) to convert the time domain signal into a signal frequency domain to determine the frequency characteristics arising from the signal. From the sound and vibration signal input data obtained, the Fuzzy logic method is used to determine the bearing condition output. The developed system is capable of detecting bearings in three conditions, namely good, damaged, and alert.

PL

Celem badań jest zaprojektowanie narzędzia, które będzie można wykorzystać do wykrywania uszkodzeń lub usterek w silnikach indukcyjnych, zwłaszcza w części łożyskowej będącej głównym elementem napędowym. Wykorzystanie czujnika akcelerometru i modułu czujnika dźwięku MPU 6050 z mikrokontrolerem Arduino Nano i modułem Bluetooth HC 05 jako medium do przesyłania i pozyskiwania danych sygnałowych. Uzyskane dane sygnałowe w postaci datalogu z rozszerzeniem .txt są następnie przetwarzane w programie Matlab w celu uzyskania informacji o charakterystyce sygnałów dźwiękowych i sygnałów wibracyjnych generowanych przez łożyska silników indukcyjnych. Zastosowanie filtra dolnoprzepustowego filtra sygnału odcinającego do przetwarzania filtra sygnału głosowego i szybkiej transformaty Fouriera (FFT) w celu konwersji sygnału w dziedzinie czasu na dziedzinę częstotliwości sygnału w celu określenia charakterystyk częstotliwości wynikających z sygnału. Na podstawie uzyskanych danych wejściowych sygnałów dźwiękowych i wibracyjnych metoda Fuzzy logic służy do określenia wyjściowego stanu łożyska. Opracowany system jest w stanie wykryć łożyska w trzech stanach: dobre, uszkodzone i czujne.

9

Bearing fault detection using a method involving absolute value spectrum and impulsivity evaluation

Bouaouiche Karim, Menasria Yamina, Khalifa Dalila

Diagnostyka

|

2024

|

Vol. 25, No. 2

art. no. 2024213

EN

This study analyzes vibration signals related to bearing defects using a method that reconstructs an effective signal. This reconstruction is based on the determination of the instantaneous amplitude and phase. Then, a decomposition method is applied to the amplitude and phase to obtain several simple functions. Once the functions are obtained, an evaluation of impulsivity is performed on each function using the proposed parameter. This selects functions that contain fault data. The important signal is then identified and used. After the creation of the effective signal, filtering by a morphological operator with a structuring element is applied to improve the signal quality. Finally, in the spectrum of the absolute values of this signal, the defect can be detected from the frequency of the peaks. Signals from different databases were analyzed using the proposed method, illustrating the results in the form of high-amplitude peaks in the frequency of bearing component defects.

10

Przebudowa mostu w Krzyżowicach z uwagi na znaczne górnicze osiadania terenu

Bętkowski Piotr

Przegląd Budowlany

|

2023

|

R. 94, nr 7-8

75--79

PL

W artykule opisano historię techniczną mostu w Krzyżowicach. Obiekt intensywnie osiadał, ale brak było istotnych przemieszczeń na łożyskach. Górnicze osiadanie terenu spowodowało znaczne zmniejszenie światła pionowego, tj. obniżenie mostu względem zwierciadła rzeki, konieczna była przebudowa mostu. Po przebudowie mostu wydłużono ściany górnicze poza obiekt, w krótkim czasie doszło do znacznych zmian w położeniu przyczółków. W artykule opisano starą konstrukcję mostu, nową konstrukcję mostu, podano wyniki długoletnich obserwacji i pomiarów oraz wskazano, jak należy projektować zabezpieczenia na wpływy górnicze w przypadku nietypowych wymuszeń górniczych.

EN

Article describes the technical history of a bridge in Krzyżowice. The object settled intensively, but there were no significant displacements on the bridge bearings. Mining subsidence caused a significant reduction in vertical light, i.e. lowering the bridge in relation to the river level, it was necessary to rebuild this bridge. After the reconstruction of the bridge, mining walls were extended beyond the object, in a short time there were significant changes in the position of the abutments. The article describes old bridge construction, new bridge construction, gives the results of long-term observations and measurements, and indicates how to design protection against mining influences in the case of unusual mining impacts.

11

Fault diagnosis of bearings based on SSWT, bayes optimisation and CNN

Yan Guohua, Hu Yihuai, Shi Qingguo

Polish Maritime Research

|

2023

|

nr 3

132--141

EN

Bearings are important components of rotating machinery and transmission systems, and are often damaged by wear, overload and shocks. Due to the low resolution of traditional time-frequency analysis for the diagnosis of bearing faults, a synchrosqueezed wavelet transform (SSWT) is proposed to improve the resolution. An improved convolutional neural network fault diagnosis model is proposed in this paper, and a Bayesian optimisation method is applied to automatically adjust the structure and hyperparameters of the model to improve the accuracy of bearing fault diagnosis. Experimental results from the accelerated life testing of bearings show that the proposed method is able to accurately identify various types of bearing fault and the different status of these faults under complex running conditions, while achieving very good generalisation ability.

12

Operating characteristics of bearings with magnetic nanoparticles doped lubricant

Krenicky Tibor, Mascenik Jozef, Coranic Tomas, Ruzbarsky Juraj

Mining Machines

|

2023

|

vol. 41, iss. 3

168--174

EN

The main aim of the presented research was to investigate theoperational characteristics of a bearing when alternative lubricants were used for comparison with a standard lubricant, including that containing magnetic nanoparticles. The bearing was subjected to varying operating conditions, differing in terms of mechanical load status. The monitoring of the bearing operation parameters primarily focused on monitoring the velocity and acceleration of vibrations, as well as the operating temperature of the bearing. Thebearingwith lubricant doped by magnetic nanoparticles exhibited reduced vibration velocity and acceleration values both under no load conditions and when subjected to a mechanicalload. The operating temperature slightly increased during testing in the case ofthe bearing with nanoparticles compared to the bearing using the original lubricant.

13

Detection of defects in a bearing by analysis of vibration signals

Bouaouiche Karim, Menasria Yamima, Khalifa Dalila

Diagnostyka

|

2023

|

Vol. 24, No. 2

art. no. 2023203

EN

This work presents the analysis of vibration signals by an approach consists of several mathematical tools more elaborate such as the Hilbert transform, kurtogram, which allows the detection of vibration defects in a simple and accurate way. The steps or methods inserted in the process one complementary to the other as scalar indicators generally used in monitoring to follow the evolution of the functioning of a machine when an abnormal functioning it must make a diagnosis to detect the failing element through the use of a process. The determination of the defective organs at an optimal time is a very important operation in the industrial maintenance, which keeps the equipment in a good condition and ensures the assiduity of work. To see the effectiveness of fault detection by the proposed approach by analyzing the real vibration signals of a bearing type 6025-SKF available on the Case Western Reserve University platform.

14

Detection and diagnosis of bearing defects using vibration signal processing

Bouaouiche Karim, Menasria Yamina, Khalfa Dalila

Archive of Mechanical Engineering

|

2023

|

LXX, nr 3

433--452

EN

This work presents an analysis of vibration signals for bearing defects using a proposed approach that includes several methods of signal processing. The goal of the approach is to efficiently divide the signal into two distinct components: a meticulously organized segment that contains relatively straightforward information, and an inherently disorganized segment that contains a wealth of intricately complex data. The separation of the two component is achieved by utilizing the weighted entropy index (WEI) and the SVMD algorithm. Information about the defects was extracted from the envelope spectrum of the ordered and disordered parts of the vibration signal. Upon applying the proposed approach to the bearing fault signals available in the Paderborn university database, a high amplitude peak can be observed in the outer ring fault frequency (45.9 Hz). Likewise, for the signals available in XJTU-SY, a peak is observed at the fault frequency (108.6 Hz).

15

A novel, machine learning-based feature extraction method for detecting and localizing bearing component defects

Cherif Bilal Djamal Eddine, Seninete Sara, Defdaf Mabrouk

Metrology and Measurement Systems

|

2022

|

Vol. 29, nr 2

333--346

EN

Vibration analysis for conditional preventive maintenance is an essential tool for the industry. The vibration signals sensored, collected and analyzed can provide information about the state of an induction motor. Appropriate processing of these vibratory signals leads to define a normal or abnormal state of the whole rotating machinery, or in particular, one of its components. The main objective of this paper is to propose a method for automatic monitoring of bearing components condition of an induction motor. The proposed method is based on two approaches with one based on signal processing using the Hilbert spectral envelope and the other approach uses machine learning based on random forests. The Hilbert spectral envelope allows the extraction of frequency characteristics that are considered as new features entering the classifier. The frequencies chosen as features are determined from a proportional variation of their amplitudes with the variation of the load torque and the fault diameter. Furthermore, a random forest-based classifier can validate the effectiveness of extracted frequency characteristics as novel features to deal with bearing fault detection while automatically locating the faulty component with a classification rate of 99.94%. The results obtained with the proposed method have been validated experimentally using a test rig.

16

Application for vibration diagnostics

Stakhova Anzhelika

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska

|

2022

|

T. 12, nr 3

46--49

EN

This paper considers the issue of developing an application for vibration diagnostics of bearings of functional pairs of critical structures, this application should help in monitoring and diagnosing bearings, using vibration signals, without disassembling the functional unit itself. It is known that vibration diagnostics is effective and there is a tendency to reduce the cost of its implementation. Monitoring and diagnostics based on vibration parameters can be applied at any time, even after several years of equipment operation, when the costs of preventive maintenance and repair will exceed the economically justified value. Also, in the work, the basics of the subject area for the development of mobile applications are considered, and a review of existing solutions is made. Requirements for the application for performing vibration diagnostics are formulated. The architecture is designed and the data description for the application of vibration diagnostics is carried out.

PL

W artykule poruszono problematykę opracowania aplikacji do diagnostyki wibracyjnej łożysk par funkcjonalnych konstrukcji krytycznych, która to aplikacja powinna pomóc w monitorowaniu i diagnozowaniu łożysk z wykorzystaniem sygnałów wibracyjnych, bez demontażu samego zespołu funkcjonalnego. Wiadomo, że diagnostyka wibracyjna jest skuteczna i istnieje tendencja do obniżania kosztów jej wykonania. Monitoring i diagnostyka na podstawie parametrów drgań może być stosowana w dowolnym momencie, nawet po kilku latach eksploatacji urządzeń, gdy koszty obsługi prewencyjnej i napraw przekroczą ekonomicznie uzasadnioną wartość. W pracy rozważane są również podstawy tematyki tworzenia aplikacji mobilnych oraz dokonywany jest przegląd istniejących rozwiązań. Sformułowano wymagania dla aplikacji do wykonywania diagnostyki wibracyjnej. Zaprojektowano architekturę i wykonano opis danych dla aplikacji diagnostyki wibracyjnej.

17

Bearing fault detection and diagnosis based on densely connected convolutional networks

Niyongabo Julius, Zhang Yingjie, Ndikumagenge Jérémie

Acta Mechanica et Automatica

|

2022

|

Vol. 16, no. 2

130--135

EN

Rotating machines are widely used in today’s world. As these machines perform the biggest tasks in industries, faults are naturally observed on their components. For most rotating machines such as wind turbine, bearing is one of critical components. To reduce failure rate and increase working life of rotating machinery it is important to detect and diagnose early faults in this most vulner-able part. In the recent past, technologies based on computational intelligence, including machine learning (ML) and deep learning (DL), have been efficiently used for detection and diagnosis of bearing faults. However, DL algorithms are being increasingly favoured day by day because of their advantages of automatically extracting features from training data. Despite this, in DL, adding neural layers reduces the training accuracy and the vanishing gradient problem arises. DL algorithms based on convolutional neural networks (CNN) such as DenseNet have proved to be quite efficient in solving this kind of problem. In this paper, a transfer learning consisting of fine-tuning DenseNet-121 top layers is proposed to make this classifier more robust and efficient. Then, a new intelligent model inspired by DenseNet-121 is designed and used for detecting and diagnosing bearing faults. Continuous wavelet transform is applied to enhance the dataset. Experimental results obtained from analyses employing the Case Western Reserve University (CWRU) bearing dataset show that the proposed model has higher diagnostic performance, with 98% average accuracy and less complexity.

18

Methods for assessing the technical condition of bearing hubs in means of transport

Stanik Zbigniew, Kubik Andrzej, Hadryś Damian, Csiszár Csaba

Zeszyty Naukowe. Transport / Politechnika Śląska

|

2021

|

z. 113

191--204

EN

This article presents two methods of testing bearing hubs, which may supplement the existing subjective and unreliable methods of diagnostics of rolling bearings used in wheel bearing hubs of motor vehicles and other means of road transport. One of the most important elements responsible for the safety of a vehicle is the bearing hub. Regular monitoring of the technical condition of bearings should become an obligation at vehicle inspection stations when carrying out a technical inspection of a vehicle, authorising it to travel on public roads. This article presents the results of vehicle tests with signs of damage to rolling bearings, using two test stands: one on which the dynamic balancer acted as a device for accelerating the wheel, and the other, which was designed as a test dedicated to automotive rolling bearings, where a dynamic weighbridge was used as the wheel drive, made it impossible to test the wheel at lower rotational speeds. The newly designed and manufactured bearing testing device eliminates the disadvantages of the previous stand, and additionally, enables the measurement of a fully loaded bearing hub, which enables the simulation of real operating conditions on the bearing hub.

19

Impact of selected operational parameters on measures of technical condition of rolling bearings in means of transport built based on analysis of vibration signals

Kubik Andrzej, Stanik Zbigniew, Hadryś Damian, Csiszár Csaba

Zeszyty Naukowe. Transport / Politechnika Śląska

|

2021

|

z. 111

89--98

EN

This article presents an innovative method of diagnostics of rolling bearings used in the bearing nodes of motor vehicles, with the use of a prototype specialist stand. The tests were carried out based on a developed research plan, which included the impact of damage to the bearing and tyre of the vehicle, as well as the vehicle speed. Vibration accelerations were recorded in three measurement axes. Signal spectra were created based on the time courses of the vibration signals and were further analysed. The presented method is aimed at detecting excessive wear of rolling bearings in wheels from its early period.

20

Method of Measuring Frictional Resistance In Pendular Motion

Opałka Mariusz

Tribologia

|

2021

|

nr 1

15--19

EN

This paper presents a methodology for conducting tribological sliding tests based on decaying vibrations in pendular motion. The proposed method of determining the (averaged) coefficient of friction in pendular motion is based on measuring the potential kinetic energy. The method is characterized by a short measuring time and enables a quick comparison of the friction coefficients of different materials.

PL

W artykule przedstawiono metodykę prowadzenia badań tribologicznych materiałów ślizgowych na podstawie drgań gasnących w ruchu wahadłowym. Zaproponowana metoda wyznaczania współczynnika tarcia (uśrednionego) w ruchu wahadłowym opiera się na pomiarze energii potencjalno-kinetycznej. Zaproponowana metoda cechuje się krótkim czasem prowadzenia pomiarów oraz umożliwia szybkie porównanie współczynników tarcia dla różnych materiałów.