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Thematische Bibliographien / Condition monitoring of a bearing / Zeitschriftenartikel

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Zeitschriftenartikel zum Thema „Condition monitoring of a bearing“

Autor: Grafiati

Veröffentlicht am 28. Juni 2021

Zuletzt aktualisiert am 29. Juli 2025

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1

Entezami, Mani, Clive Roberts, Paul Weston, Edward Stewart, Arash Amini, and Mayorkinos Papaelias. "Perspectives on railway axle bearing condition monitoring." Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 234, no. 1 (2019): 17–31. http://dx.doi.org/10.1177/0954409719831822.

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Defects in railway axle bearings can affect operational efficiency, or cause in-service failures, damaging the track and train. Healthy bearings produce a certain level of vibration and noise, but a bearing with a defect causes substantial changes in the vibration and noise levels. It is possible to detect the bearing defects at an early stage of their development, allowing an operator to repair the damage before it becomes serious. When a vehicle is scheduled for maintenance, or due for overhaul, knowledge of bearing damage and severity is beneficial, resulting in fewer operational problems a

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Lin, Hui Bin, and Kang Ding. "Rolling Element Bearing Condition Monitoring and Diagnosis." Applied Mechanics and Materials 34-35 (October 2010): 332–37. http://dx.doi.org/10.4028/www.scientific.net/amm.34-35.332.

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Bearing failure is one of the foremost causes of breakdown in rotating machinery. To date, Envelope detection is always used to identify faults occurring at the Bearing Characteristic Frequencies (BCF). However, because the impact vibration generated by a bearing fault has relatively low energy, it is often overwhelmed by background noise and difficult to identify. Combined the results of extensive experiments performed in a series of bearings with artificial damage, this research investigates the effect of many influencing factors, such as demodulation methods, sampling frequency, variable ma

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Hohmann, M., E. Blechschmidt, and O. Menck. "Prediction of the friction torque of scaled blade bearings in a test rig using machine learning." Journal of Physics: Conference Series 2767, no. 5 (2024): 052010. http://dx.doi.org/10.1088/1742-6596/2767/5/052010.

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Abstract Blade bearing friction torque is a required parameter for the design of a pitch actuator, and deviations from a bearing’s initial torque can be utilized for condition monitoring of the bearing. The torque of large-scale bearings can, however, be difficult to predict due to quality fluctuations in the production of these large-scale components. Therefore, this paper employs machine learning approaches to predict the torque of a given set of bearings in a controlled test environment based on measurement data from that same set of bearings. Possible applications of the model include use

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Gouws, R. "Active magnetic bearing condition monitoring." World Journal of Engineering 10, no. 2 (2013): 179–88. http://dx.doi.org/10.1260/1708-5284.10.2.179.

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Bai, Xiu Qin, Han Liang Xiao, and Lu Zhang. "The Condition Monitoring of Large Slewing Bearing Based on Oil Analysis Method." Key Engineering Materials 474-476 (April 2011): 716–19. http://dx.doi.org/10.4028/www.scientific.net/kem.474-476.716.

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Large slewing bearing is a special kind of rolling bearing with heavy load and very low rotation speed. It is important to carry out faults monitoring on this kind of rolling bearing. However, it is difficult to carry out vibration monitoring on such large slewing bearing. The running conditions of slewing bearings of ship loader and stacking crane in Qinghuangdao Port were analyzed using ferrography and spectrometric analysis technology. Monitoring results showed that the slewing bearing of SL-Q1 ship loader was under abnormal wear condition. Further inspection indicated that the rolling elem

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Wu, Zian, Wenxian Yang, Xiaoping Song, and Kexiang Wei. "Study on Condition Monitoring of Pitch Bearings Based on Stress Measurement." Machines 12, no. 3 (2024): 154. http://dx.doi.org/10.3390/machines12030154.

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Pitch bearings in wind turbines are crucial components that enable safe blade pitching, optimize electrical power output, and ensure turbine protection. Traditional vibration analysis-based methods used for high-speed bearings are not applicable to monitoring pitch bearings, due to its slow non-integer cycle rotation. To address this issue, a stress-based pitch bearing monitoring method is proposed in this paper. First, finite element analysis is conducted to establish the relationship between the maximum surface stress on the outer race of the pitch bearing and the presence of cracks. This re

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Singh, R. C., R. K. Pandey, Rajiv Chaudhary, and Ranganath M. S. "Analysis of Ball Bearings under Dynamic Loading Using Non- Destructive Technique of Thermography." International Journal of Advance Research and Innovation 2, no. 4 (2014): 118–21. http://dx.doi.org/10.51976/ijari.241417.

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Roller bearing defect is a major factor of failure in rotating machinery that affects proper functioning of system which results in substantial time and economic losses. Therefore, condition monitoring of roller bearing is important and the study of severity of defects are necessarily required in order to avoid catastrophic consequences. Defects monitoring during dynamic loading conditions of rotational machineries with the use of contactless, non-destructive infrared thermographic method is proposed. By using a rotating ball bearing, passive thermographic experiment was performed as an altern

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Sandoval, Diego, Urko Leturiondo, Yolanda Vidal, and Francesc Pozo. "Entropy Indicators: An Approach for Low-Speed Bearing Diagnosis." Sensors 21, no. 3 (2021): 849. http://dx.doi.org/10.3390/s21030849.

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To increase the competitiveness of wind energy, the maintenance costs of offshore floating and fixed wind turbines need to be reduced. One strategy is the enhancement of the condition monitoring techniques for pitch bearings, because their low operational speed and the high loads applied to them make their monitoring challenging. Vibration analysis has been widely used for monitoring the bearing condition with good results obtained for regular bearings, but with difficulties when the operational speed decreases. Therefore, new techniques are required to enhance the capabilities of vibration an

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Irfan, Muhammad, Faisal Althobiani, Abdullah Saeed Alwadie, et al. "Condition monitoring of water pump bearings using ensemble classifier." Advances in Mechanical Engineering 14, no. 3 (2022): 168781322210891. http://dx.doi.org/10.1177/16878132221089170.

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The bearings faults are reported to be the major reason for centrifugal pump (CPs) failures. Limited literature is available to diagnose the minor scratches in the bearing surface through non-intrusive condition monitoring techniques. Recent research on the analysis of bearing scratches through non-intrusive motor current analysis (MCA) has shown encouraging results where the comparison of machine learning and convolutional neural networks (CNNs) was performed in the classification of healthy bearings and faulty bearings (holes and scratches). The fault classification accuracy of 89.26% throug

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Patel, R. K., and V. K. Giri. "Condition monitoring of induction motor bearing based on bearing damage index." Archives of Electrical Engineering 66, no. 1 (2017): 105–19. http://dx.doi.org/10.1515/aee-2017-0008.

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Abstract The rolling element bearings are used broadly in many machinery applications. It is used to support the load and preserve the clearance between stationary and rotating machinery elements. Unfortunately, rolling element bearings are exceedingly prone to premature failures. Vibration signal analysis has been widely used in the faults detection of rotating machinery and can be broadly classified as being a stationary or non-stationary signal. In the case of the faulty rolling element bearing the vibration signal is not strictly phase locked to the rotational speed of the shaft and become

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Li, C. J., J. Ma, and B. Hwang. "Bearing Condition Monitoring by Pattern Recognition Based on Bicoherence Analysis of Vibrations." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 210, no. 3 (1996): 277–85. http://dx.doi.org/10.1243/pime_proc_1996_210_197_02.

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For automatic detection and diagnosis of localized defects in rolling element bearings, bicoherence spectra are used to derive features that signify the condition of a bearing. These features quantitatively describe the degree of phase correlation among any three harmonics of bearing characteristic defect frequencies. Employing these features, a linear discriminant classifier is implemented to detect localized defects on a roller and the outer race of a bearing. Experimental results show that the proposed scheme is effective in bearing defect detection and sensitive to incipient defects.

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Liu, Rende. "Condition monitoring of low‐speed and heavily loaded rolling element bearing." Industrial Lubrication and Tribology 59, no. 6 (2007): 297–300. http://dx.doi.org/10.1108/00368790710820892.

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PurposeThis paper sets out to develop a reliable analysis method based upon a low‐cost procedure to monitor the wear condition of low‐speed and heavily loaded rolling element bearing.Design/methodology/approachSpecial solvents for grease are invented and new test methods, including spectroscopy and ferrography of used grease, are developed to monitor the wear condition of a deferred bearing of ladle turret in continuous casting.FindingsAccording to the analytical results, the service life of the ladle turret bearing in No. 1 continuous casting machine is extended to 14 years and significant ex

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Zhang, Yanfei, Yunhao Li, Lingfei Kong, Qingbo Niu, and Yu Bai. "Improved DBSCAN Spindle Bearing Condition Monitoring Method Based on Kurtosis and Sample Entropy." Machines 10, no. 5 (2022): 363. http://dx.doi.org/10.3390/machines10050363.

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An improved density-based spatial clustering of applications with noise (IDBSCAN) analysis approach based on kurtosis and sample entropy (SE) is presented for the identification of operational state in order to provide accurate monitoring of spindle operation condition. This is because of the low strength of the shock signal created by bearing of precision spindle of misalignment or imbalanced load, and the difficulties in extracting shock features. Wavelet noise reduction begins by dividing the recorded vibration data into equal lengths. Features like kurtosis and entropy in the frequency dom

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Gaikwad, Mahesh, Piyush Shakya, and Sivasrinivasu Devadula. "Condition Monitoring of Ball Bearing Using MEMS-based Accelerometer." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 268, no. 6 (2023): 2795–801. http://dx.doi.org/10.3397/in_2023_0406.

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Industry 4.0 has attracted a lot of attention in recent times. Intelligent machines are at the heart of Industry 4.0. Condition monitoring and predictive maintenance of critical components in rotating machines are crucial for intelligent machines. This paper studies the use of a commercially available Micro Electro Mechanical System (MEMS) accelerometer for the condition monitoring of ball bearings. Embedding the sensor in the housing reduces the transmission path between the sensor and the fault providing efficient condition monitoring for low-speed applications. A comparison between MEMS and

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Hariharan, V., and P. S. S. Srinivasan. "Condition monitoring studies on ball bearings considering solid contaminants in the lubricant." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 224, no. 8 (2010): 1727–48. http://dx.doi.org/10.1243/09544062jmes1885.

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Rolling element bearings are common in any rotating machinery. They are subject to failure under continuous running. Therefore they have received a great deal of attention in the field of condition monitoring. In rolling element bearings, contamination of lubricant grease by solid particles is one of the several reasons for an early bearing failure. In this context, this article investigates the effect of contamination of lubricant by solid particles on the dynamic behaviour of rolling bearings. Silica powder at three concentration levels and different particle sizes was used to contaminate th

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Jamaludin, N., D. Mba, and R. H. Bannister. "Condition monitoring of slow-speed rolling element bearings using stress waves." Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering 215, no. 4 (2001): 245–71. http://dx.doi.org/10.1177/095440890121500401.

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Condition monitoring of rolling element bearings through the use of vibration analysis is an established technique for detecting early stages of component degradation. However, this success is not mirrored at rotational speeds below 16r/min. At such speeds the energy generated from bearing defects might not show as an obvious change in signature and thus becomes undetectable using conventional vibration measuring equipment. This paper presents an investigation into the applicability of stress wave analysis for detecting early stages of bearing damage at a rotational speed of 1.12r/min (0.0187

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Lei, Lei, Dongli Song, Zhendong Liu, Xiao Xu, and Zejun Zheng. "Displacement Identification by Computer Vision for Condition Monitoring of Rail Vehicle Bearings." Sensors 21, no. 6 (2021): 2100. http://dx.doi.org/10.3390/s21062100.

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Bearings of rail vehicles bear various dynamic forces. Any fault of the bearing seriously threatens running safety. For fault diagnosis, vibration and temperature measured from the bogie and acoustic signals measured from trackside are often used. However, installing additional sensing devices on the bogie increases manufacturing cost while trackside monitoring is susceptible to ambient noise. For other application, structural displacement based on computer vision is widely applied for deflection measurement and damage identification of bridges. This article proposes to monitor the health cond

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Tarawneh, Constantine. "Preventing hot bearing derailments via wireless onboard condition monitoring." Journal of the Acoustical Society of America 156, no. 4_Supplement (2024): A96. https://doi.org/10.1121/10.0035227.

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The 2023 train derailment that occurred in East Palestine, OH, brought attention to the limitations of the detectors currently used in the industry. Typically, the health of train bearings is monitored intermittently through wayside temperature detection systems that can be as far as 40 miles apart. Nonetheless, catastrophic bearing failure is often sudden and develops rapidly. Current wayside detection systems are reactive in nature and depend on significant temperature increases above ambient, which, when detected, train operators have little time to react before a derailment occurs, as it d

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Ye, Lin, Zhuang Lin, Liang Xuan, et al. "The Research on Condition Monitoring and Fault Diagnosis Method of Wind Power Spindle Bearing." Journal of Physics: Conference Series 2254, no. 1 (2022): 012048. http://dx.doi.org/10.1088/1742-6596/2254/1/012048.

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Abstract With the continuous and substantial increase in the demand for wind power generation in China, the development of wind power generation units tends to be large-scale, intelligent and efficient. Besides, the working environment of spindle bearings is relatively bad and the failure rate is relatively high. Therefore, it is necessary to conduct state monitoring and fault diagnosis for wind power generation units. This paper first introduces the bearing system of wind turbine, then expounds the common faults of wind turbine spindle bearing, and carries out relevant research. According to

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Wan, Li Rong, Guang Yu Zhou, Cheng Long Wang, and Wen Ming Zhao. "Mine Hoist Bearing Condition Monitoring and Fault Diagnosis System Based on Labview." Advanced Materials Research 317-319 (August 2011): 1232–36. http://dx.doi.org/10.4028/www.scientific.net/amr.317-319.1232.

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By taking full advantage of the technologies of data acquisition, signal analysis and processing and fault diagnosis, this thesis carries out a research on the realization method of mine hoist bearing condition monitoring and fault diagnosis. Firstly, this thesis takes a technical analysis for rolling bearing. Secondly, based on determining the overall framework and using a virtual instrument software (Labview), it carries out a program development of the system. The developed system not only integrates the functions of traditional instruments, but also describes the bearing states and the typ

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Kannan, Vigneshwar, Tieling Zhang, and Huaizhong Li. "A Review of the Intelligent Condition Monitoring of Rolling Element Bearings." Machines 12, no. 7 (2024): 484. http://dx.doi.org/10.3390/machines12070484.

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Bearing component damage contributes significantly to rotating machinery failures. It is vital for the rotor-bearing system to be in good condition to ensure the proper functioning of the machine. Over recent decades, extensive research has been devoted to the condition monitoring of rotational machinery, with a particular focus on bearing health. This paper provides a comprehensive literature review of recent advancements in intelligent condition monitoring technologies for rolling element bearings. Fundamental monitoring strategies are introduced, covering various sensing, signal processing,

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Rabeyee, K., X. Tang, F. Gu, and A. D. Ball. "The Effect of Wear Evolution on Vibration-based Fault Detection in Tapered Roller Bearings." International Journal of Condition Monitoring 9, no. 1 (2019): 18–23. http://dx.doi.org/10.1784/204764297237736057.

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Rolling element bearings (REBs) are typical tribological components used widely in rotating machines. Their failure could cause catastrophic damage. Therefore, condition monitoring of bearings has always had great appeal for researchers. Usually, the detection and diagnostics of incipient bearing faults are achieved by characterising the weak periodic impacts induced by the collision of defective bearing components. However, race wear evolution, which is inevitable in bearing applications, can affect the contact between bearing elements and races, thereby decreasing the impact magnitudes and i

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GINZINGER, L., M. N. SAHINKAYA, T. SCHINDLER, H. ULBRICH, and P. KEOGH. "4A16 Model-Based Condition Monitoring of an Auxiliary Bearing following Contact Events." Proceedings of the Symposium on the Motion and Vibration Control 2010 (2010): _4A16–1_—_4A16–16_. http://dx.doi.org/10.1299/jsmemovic.2010._4a16-1_.

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Brito Junior, Geraldo Carvalho, Roberto Dalledone Machado, Anselmo Chaves Neto, and Mateus Feiertag Martini. "Experimental Aspects in the Vibration-Based Condition Monitoring of Large Hydrogenerators." International Journal of Rotating Machinery 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/1805051.

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Based on experimental observations on a set of twenty 700 MW hydrogenerators, compiled from several technical reports issued over the last three decades and collected from the reprocessing of the vibration signals recorded during the last commissioning tests, this paper shows that the accurate determination of the journal bearings operating conditions may be a difficult task. It shows that the outsize bearing brackets of large hydrogenerators are subject to substantial dimensional changes caused by external agents, like the generator electromagnetic field and the bearing cooling water temperat

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Laubichler, Christian, Constantin Kiesling, Matheus Marques da Silva, Andreas Wimmer, and Gunther Hager. "Data-Driven Sliding Bearing Temperature Model for Condition Monitoring in Internal Combustion Engines." Lubricants 10, no. 5 (2022): 103. http://dx.doi.org/10.3390/lubricants10050103.

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Condition monitoring of components in internal combustion engines is an essential tool for increasing engine durability and avoiding critical engine operation. If lubrication at the crankshaft main bearings is insufficient, metal-to-metal contacts become likely and thus wear can occur. Bearing temperature measurements with thermocouples serve as a reliable, fast responding, individual bearing-oriented method that is comparatively simple to apply. In combination with a corresponding reference model, such measurements could serve to monitor the bearing condition. Based on experimental data from

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Weng Zhen, Lim, Anwar P.P Abdul Majeed, Mohd Azraai Mohd Razman, and Ahmad Fakhri Ab. Nasir. "The Condition Based Monitoring for Bearing Health." MEKATRONIKA 2, no. 1 (2020): 63–67. http://dx.doi.org/10.15282/mekatronika.v2i1.6735.

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Bearing is a small component that widely uses in industries, either in rotary machines or shafts. Faulty in bearing might cause massive downtime in the industries, which lead to loss of revenue. This paper intends to find the consequential statistical time-domain-based features that can be used in classification from accelerometry signals for the bearing condition. An accelerometer was used as the data logger device to attain the condition signals from the bearing. Machinery Failure Prevention Technology (MFPT) online dataset has three different bearing conditions: baseline condition, inner fa

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James Li, C., and S. Y. Li. "Acoustic emission analysis for bearing condition monitoring." Wear 185, no. 1-2 (1995): 67–74. http://dx.doi.org/10.1016/0043-1648(95)06591-1.

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Zhang, Jingyi, and Rui Bai. "Determining the start to degradation time of bearings using the improved diversity entropy." Journal of Physics: Conference Series 2762, no. 1 (2024): 012032. http://dx.doi.org/10.1088/1742-6596/2762/1/012032.

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Abstract Rolling element bearing is an essential part of rotating components. If the rolling element bearing is broken, it may cause damage to the system and even pose a threat to life health, and property safety. Therefore, the rolling element bearing’s health condition needs to be monitored and forecasted, thereby reducing unplanned downtime and improving bearing productivity. It is also important to determine the start to degradation time (SDT) when monitoring and predicting the condition of rolling bearings. Determining the SDT can help monitor the degradation process of the bearing so tha

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Xiangyang, Li, and Chen Wanqiang. "Rolling Bearing Fault Diagnosis Based on Physical Model and One-Class Support Vector Machine." ISRN Mechanical Engineering 2014 (April 14, 2014): 1–4. http://dx.doi.org/10.1155/2014/160281.

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This paper aims at diagnosing the fault of rolling bearings and establishes the system of dynamics model with the consideration of rolling bearing with nonlinear bearing force, the radial clearance, and other nonlinear factors, using Runge-Kutla such as Hertzian elastic contactforce and internal radial clearance, which are solved by the Runge-Kutta method. Using simulated data of the normal state, a self-adaptive alarm method for bearing condition based on one-class support vector machine is proposed. Test samples were diagnosed with a recognition accuracy over 90%. The present method is furth

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Wu, Jun, Chaoyong Wu, Yaqiong Lv, Chao Deng, and Xinyu Shao. "Design a degradation condition monitoring system scheme for rolling bearing using EMD and PCA." Industrial Management & Data Systems 117, no. 4 (2017): 713–28. http://dx.doi.org/10.1108/imds-11-2016-0469.

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Purpose Rolling bearings based on rotating machinery are one of the most widely used in industrial applications because of their low cost, high performance and robustness. The purpose of this paper is to describe how to identify degradation condition of rolling bearing and predict its fault time in big data environment in order to achieve zero downtime performance and preventive maintenance for the rolling bearing. Design/methodology/approach The degradation characteristic parameters of rolling bearings including intrinsic mode energy and failure frequency were, respectively, extracted from th

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Zhang, Yanfei, Yang Liu, Lijie Wang, Dongya Li, Wenxue Zhang, and Lingfei Kong. "Bearing Non-Uniform Loading Condition Monitoring Based on Dual-Channel Fusion Improved DenseNet Network." Lubricants 11, no. 6 (2023): 251. http://dx.doi.org/10.3390/lubricants11060251.

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Misalignment or unbalanced loading of machine tool spindle bearings often results in skewed bearing operation, which makes the spindle more susceptible to failure. In addition, due to the weak impact signal of the bearing in skewed operation, a single feature information cannot accurately characterize the operation status of the bearing. To address the above problems, this paper proposes a method to monitor the uneven running state of bearing load based on a dual-channel fusion improved dense connection (DenseNet) network. First, the original signal is pre-processed by overlapping sampling met

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Hong, Lee Chun, Abd Kadir Mahamad, and Sharifah Saon. "RetComm 1.0: Real Time Condition Monitoring of Rotating Machinery Failure." MATEC Web of Conferences 150 (2018): 01002. http://dx.doi.org/10.1051/matecconf/201815001002.

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The breakdown of motor proves to be very expensive as it increases downtime on the machines. Development of cost-effective and reliable condition monitoring system for the protection of motors to avoid unexpected breakdowns is necessary. Therefore, RetComm 1.0 is developed as assistant tool for bearing condition diagnosis system. The smartphone accelerometer is used to collect the vibration signal data and send it to computer by using the Android application named Matlab Mobile. The Matlab software is used to implement a program which is the RetComm 1.0 system to analyse the vibration signal a

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Nasim Khan Raja, Babar, Saeed Miramini, Colin Duffield, Shilun Chen, and Lihai Zhang. "A Simplified Methodology for Condition Assessment of Bridge Bearings Using Vibration Based Structural Health Monitoring Techniques." International Journal of Structural Stability and Dynamics 21, no. 10 (2021): 2150133. http://dx.doi.org/10.1142/s0219455421501339.

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The mechanical properties of bridge bearings gradually deteriorate over time resulting from daily traffic loading and harsh environmental conditions. However, structural health monitoring of in-service bridge bearings is rather challenging. This study presents a bridge bearing condition assessment framework which integrates the vibration data from a non-contact interferometric radar (i.e. IBIS-S) and a simplified analytical model. Using two existing concrete bridges in Australia as a case study, it demonstrates that the developed framework has the capability of detecting the structural conditi

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Narendiranath Babu, T., T. Manvel Raj, and T. Lakshmanan. "A Review on Application of Dynamic Parameters of Journal Bearing for Vibration and Condition Monitoring." Journal of Mechanics 31, no. 4 (2015): 391–416. http://dx.doi.org/10.1017/jmech.2015.6.

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AbstractThe journal bearings are used to support high-speed rotors in turbo machinery which often operate above the rotor first bending critical speed. This bearing provide both lateral support and dynamic coefficients: Stiffness, damping, and mass terms, related to machine vibrations. The various methods of identifying journal bearing dynamic characteristics, from measured data, obtained from different measurement systems, are reviewed. The various approaches to the bearing identification problem are discussed. The various data processing methods in the time and frequency domains are presente

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Bhatti, Saima, Fozia Shaikh, Asif Mansoor, and Asif Ali Shaikh. "A unique statistical framework to predict the health of a machine by utilizing the vibration features of rolling element bearing data." Mehran University Research Journal of Engineering and Technology 44, no. 3 (2025): 37–41. https://doi.org/10.22581/muet1982.0166.

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Rolling element bearings are critical components in machinery that rotates, supporting radial and axial loads while facilitating smooth motion. Bearing malfunctions rank among the most frequent mechanical failures in rotating machinery, resulting in significant downtime and repair costs if not identified promptly. The Root Mean Square (RMS) value is a widely used statistical feature in Condition Monitoring (CM), providing a reliable and quantitative technique for detecting early-stage bearing faults. This study introduces a method for predicting machine health by utilizing RMS values derived f

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Bujoreanu, Carmen, and Florin Breabăn. "Bearing Scuffing Detection and Condition Monitoring Using Virtual Instrumentation." Applied Mechanics and Materials 657 (October 2014): 604–8. http://dx.doi.org/10.4028/www.scientific.net/amm.657.604.

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Bearing condition monitoring confronts the most machine users. Diagnostic methods used to include bearing problems represent one of the most important challenges. The scuffing phenomenon initiation of the bearing elements produces an important increase in the vibration level and can be emphasized by the analysis of the bearing friction forces which are the most sensitive indicator of the bearing failure. Commonly used technique for damage detection is the vibration signature analysis that must be carefully utilized in conjunction with the friction torque monitoring through the strain gauges me

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Wan, Biao, Jianguo Yang, and Qinghe Wang. "Evaluation of Tribological Properties of Bearing Materials for Marine Diesel Engines Utilising the Contact Voltage Method." Applied Sciences 11, no. 17 (2021): 7811. http://dx.doi.org/10.3390/app11177811.

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The contact voltage (CV) method, which can detect miniature failures, has been tested under laboratory conditions to monitor the condition of bearings. In this study, the bearing materials for marine diesel engines, aluminium and copper alloy, were tested on a bearing fatigue wear test bench in the boundary lubrication state, which was found through tests of the different parameters. The frictional torque, the oil film thickness and the bearing temperature were measured, as well as the CV signals. The possibility of using the CV technique to monitor the condition of the bearings was also asses

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Grishchenko, A. V., and О. R. Khamidov. "Monitoring and diagnostics of the technical condition of the asynchronous traction motor of locomotives using artificial neural networks on the railways of the Republic of Uzbekistan." Proceedings of Petersburg Transport University 17, no. 4 (2020): 514–24. http://dx.doi.org/10.20295/1815-588x-2020-4-514-524.

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Objective: Diagnostics of malfunctions of rolling bearings of an asynchronous traction electric motor (ATEM) of locomotives using artifi cial neural networks. Methods: To control and diagnose the technical condition of the ATEM bearing units of locomotives, a hardware-software complex and data analysis methods are used. Results: We investigated the malfunctions of the ATEM rolling bearing of locomotives. The analysis of failures of locomotive bearing units is carried out. Vibration and current signals and the corresponding frequency spectra of an ATEM operating under normal conditions and with

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Manjunatha, G., and H. C. Chittappa. "Bearing Fault Classification using Empirical Mode Decomposition and Machine Learning Approach." Journal of Mines, Metals and Fuels 70, no. 4 (2022): 214. http://dx.doi.org/10.18311/jmmf/2022/30060.

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Industrial machinery often breakdowns due to faults in rolling bearing. Bearing diagnosis plays a vital role in condition monitoring of machinery. Operating conditions and working environment of bearings make them prone to single or multiple faults. In this research, signals from both healthy and faulty bearings are extracted and decomposed into empirical modes. By analyzing different empirical modes from 8 derived empirical modes for healthy and faulty bearings under different fault sizes, the first mode has the most information to classify bearing condition. From the first empirical mode eig

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Sahoo, Sudarsan, J. K. Das, and Bapi Debnath. "Rolling Element Bearing Condition Monitoring using Filtered Acoustic Emission." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (2018): 3560. http://dx.doi.org/10.11591/ijece.v8i5.pp3560-3567.

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The defect present in the bearing of a rolling element may affect the performance of the rotating machinery and may reduce its efficiency. For this reason the condition monitoring of a rolling element bearing is very essential. So many measuring parameters are there to diagnose the fault in a rolling element bearing. Acoustic signature monitoring is one of them. Every rolling element bearing has its own acoustic signature when it is in healthy condition and when the bearing get defected then there is a change in its original acoustic signature. This change in acoustic signature can be monitore

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Sudarsan, Sahoo, K. Das J., and Debnath Bapi. "Rolling Element Bearing Condition Monitoring using Filtered Acoustic Emission." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (2018): 3560–67. https://doi.org/10.11591/ijece.v8i5.pp3560-3567.

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The defect present in the bearing of a rolling element may affect the performance of the rotating machinery and may reduce its efficiency. For this reason the condition monitoring of a rolling element bearing is very essential. So many measuring parameters are there to diagnose the fault in a rolling element bearing. Acoustic signature monitoring is one of them. Every rolling element bearing has its own acoustic signature when it is in healthy condition and when the bearing get defected then there is a change in its original acoustic signature. This change in acoustic signature can be monitore

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Yang, Tongguang, Xingyuan Huang, Yongjian Zhang, Jinglan Li, Xianwen Zhou, and Qingkai Han. "RTCA-Net: A New Framework for Monitoring the Wear Condition of Aero Bearing with a Residual Temporal Network under Special Working Conditions and Its Interpretability." Mathematics 12, no. 17 (2024): 2687. http://dx.doi.org/10.3390/math12172687.

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The inter-shaft bearing is the core component of a high-pressure rotor support system of a high-thrust aero engine. One of the most challenging tasks for a PHM is monitoring its working condition. However, considering that in the bearing rotor system of a high-thrust aero engine bearings are prone to wear failure due to unbalanced or misaligned faults of the rotor system, especially in harsh environments, such as those at high operating loads and high rotation speeds, bearing wear can easily evolve into serious faults. Compared with aero engine fault diagnosis and RUL prediction, relatively li

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Miao, Yijun, Shuai Gao, Yun Kong, Ziyuan Jiang, Qinkai Han, and Fulei Chu. "Variable reluctance bearing generators applicable in condition monitoring of bearing cages." Mechanical Systems and Signal Processing 194 (July 2023): 110249. http://dx.doi.org/10.1016/j.ymssp.2023.110249.

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Georgiadis, Anthimos, Xiaoyun Gong, and Nicolas Meier. "Vibration analysis based on the spectrum kurtosis for adjustment and monitoring of ball bearing radial clearance." MATEC Web of Conferences 211 (2018): 06006. http://dx.doi.org/10.1051/matecconf/201821106006.

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Vibration signal analysis is a common tool to detect bearing condition. Effective methods of vibration signal analysis should extract useful information for bearing condition monitoring and fault diagnosis. Spectral kurtosis (SK) represents one valuable tool for these purposes. The aim of this paper is to study the relationship between bearing clearance and bearing vibration frequencies based on SK method. It also reveals the effect of the bearing clearance on the bearing vibration characteristic frequencies This enables adjustment of bearing clearance in situ, which could significantly affect

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Elforjani, Mohamed. "Diagnosis and prognosis of slow speed bearing behavior under grease starvation condition." Structural Health Monitoring 17, no. 3 (2017): 532–48. http://dx.doi.org/10.1177/1475921717704620.

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The monitoring and diagnosis of rolling element bearings with acoustic emission and vibration measurements has evolved as one of the much used techniques for condition monitoring and diagnosis of rotating machinery. Furthermore, recent developments indicate the drive toward integration of diagnosis and prognosis algorithms in future integrated machine health management systems. With this in mind, this article is an experimental study of slow speed bearings in a starved lubricated contact. It investigates the influence of grease starvation conditions on detection and monitoring natural defect i

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Cornel, Daniel, Francisco Gutiérrez Guzmán, Georg Jacobs, and Stephan Neumann. "Condition monitoring of roller bearings using acoustic emission." Wind Energy Science 6, no. 2 (2021): 367–76. http://dx.doi.org/10.5194/wes-6-367-2021.

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Abstract. Roller bearing failures in wind turbines' gearboxes lead to long downtimes and high repair costs, which could be reduced by the implementation of a predictive maintenance strategy. In this paper and within this context, an acoustic-emission-based condition monitoring system is applied to roller bearing test rigs with the aim of identifying critical operating conditions before bearing failures occurs. Furthermore, a comparison regarding detection times is carried out with traditional vibration-based condition monitoring systems, with a focus on premature bearing failures such as white

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Jamil, Mohd Atif, and Sidra Khanam. "Fault Classification of Rolling Element Bearing in Machine Learning Domain." International Journal of Acoustics and Vibration 27, no. 2 (2022): 77–90. http://dx.doi.org/10.20855/ijav.2022.27.21829.

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Rolling element bearings are crucial components of rotating machinery used in various industries, including aerospace, navigation, machine tools, etc. Therefore, it is essential to establish suitable techniques for condition monitoring and fault diagnosis of bearings to avoid breakdowns and damages during operation for overall industrial sustainability. Vibration-based condition monitoring has been the most employed technique in this perspective. Many researchers have investigated the vibration response of rolling element bearings having inner race defects, outer race defects, or rolling eleme

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Gruber, Hannes, Anna Fuchs, and Michael Bader. "Evaluation of a Condition Monitoring Algorithm for Early Bearing Fault Detection." Sensors 24, no. 7 (2024): 2138. http://dx.doi.org/10.3390/s24072138.

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Roller bearings are critical components in various mechanical systems, and the timely detection of potential failures is essential for preventing costly downtimes and avoiding substantial machinery breakdown. This research focuses on finding and verifying a robust method that can detect failures early, without creating false positive failure states. Therefore, this paper introduces a novel algorithm for the early detection of roller bearing failures, particularly tailored to high-precision bearings and automotive test bed systems. The featured method (AFI—Advanced Failure Indicator) utilizes t

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Ramesh Aher, Harshal, and Nilesh C. Ghuge. "Vibration-Based Condition Monitoring of Tapered Roller Bearings Using Kurtosis and ANOVA." Tribology in Industry 47, no. 2 (2025): 370–88. https://doi.org/10.24874/ti.1934.04.25.06.

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This study develops a robust diagnostic framework for early fault detection in tapered roller bearings (TRBs) using vibration-based analysis. It explores the effect of operating parameters on vibration kurtosis to enable fault diagnosis in the 30205J2/Q, 30206J2/Q, and 30207J2/Q TRBs. The proposed Taguchi L27 orthogonal experimental design analyzes the effects of speed, load, unbalance, bearing type, and defect severity on kurtosis. Both the Inner Race Defect Model (IRDM) and Outer Race Defect Model (ORDM) demonstrate high predictive accuracy with R² values of 98.68% and 97.61% respectively. A

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STANIK, Zbigniew, Andrzej KUBIK, Damian HADRYŚ, and Csaba CSISZÁR. "METHODS FOR ASSESSING THE TECHNICAL CONDITION OF BEARING HUBS IN MEANS OF TRANSPORT." Scientific Journal of Silesian University of Technology. Series Transport 113 (December 1, 2021): 191–204. http://dx.doi.org/10.20858/sjsutst.2021.113.15.

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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 test

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