Relevant bibliographies by topics / Inner race bearing fault

Academic literature on the topic 'Inner race bearing fault'

Author: Grafiati
Published: 11 March 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Contents

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Inner race bearing fault.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Inner race bearing fault"

1

Fei, Cheng-Wei, Yat-Sze Choy, Guang-Chen Bai, and Wen-Zhong Tang. "Multi-feature entropy distance approach with vibration and acoustic emission signals for process feature recognition of rolling element bearing faults." Structural Health Monitoring 17, no. 2 (January 24, 2017): 156–68. http://dx.doi.org/10.1177/1475921716687167.

Full text
Abstract:
To accurately reveal rolling bearing operating status, multi-feature entropy distance method was proposed for the process character analysis and diagnosis of rolling bearing faults by the integration of four information entropies in time domain, frequency domain and time–frequency domain and two kinds of signals including vibration signals and acoustic emission signals. The multi-feature entropy distance method was investigated and the basic thought of rolling bearing fault diagnosis with multi-feature entropy distance method was given. Through rotor simulation test rig, the vibration and acoustic emission signals of six rolling bearing faults (ball fault, inner race fault, outer race fault, inner ball faults, inner–outer faults and normal) are gained under different rotational speeds. In the view of the multi-feature entropy distance method, the process diagnosis of rolling bearing faults was implemented. The analytical results show that multi-feature entropy distance fully reflects the process feature of rolling bearing faults with the change of rotating speed; the multi-feature entropy distance with vibration and acoustic emission signals better reports signal features than single type of signal (vibration or acoustic emission signal) in rolling bearing fault diagnosis; the proposed multi-feature entropy distance method holds high diagnostic precision and strong robustness (anti-noise capacity). This study provides a novel and useful methodology for the process feature extraction and fault diagnosis of rolling element bearings and other rotating machinery.
APA, Harvard, Vancouver, ISO, and other styles
2

Karyatanti, Iradiratu, Firsyaldo Purnomo, Ananda Noersena, Rafli Zulkifli, Nuddin Harahab, Ratno Wibowo, Agus Budiarto, and Ardik Wijayanto. "Sound analysis to diagnosis inner race bearing damage on induction motors using fast fourier transform." Serbian Journal of Electrical Engineering 20, no. 1 (2023): 33–47. http://dx.doi.org/10.2298/sjee2301033k.

Full text
Abstract:
The induction motor is a type of electric machine that is widely used for industrial operations in this modern era. It is an alternating current electric machine with several advantages, namely cheap, simple construction, and not requiring excessive maintenance, but has the biggest percentage of motor fault in the bearings. Therefore, this study aims to identify the inner race-bearing fault detection system based on sound signal frequency analysis. The sound signal processing was carried out using the Fast Fourier Transform (FFT) algorithm to analyze the condition of the inner race-bearing. The sound signal was used because it does not require direct contact with the bearing (non-invasive). The fault detection system was tested with two defects, namely scratched inner race and perforated inner race bearing. The results gave a successful detection of the condition of the inner race bearing with a percentage of 81.24%. This showed that the fault detection system using sound signals with FFT signal processing was carried out with high accuracy.
APA, Harvard, Vancouver, ISO, and other styles
3

Wang, Hongchao. "Fault diagnosis of rolling element bearing compound faults based on sparse no-negative matrix factorization-support vector data description." Journal of Vibration and Control 24, no. 2 (March 10, 2016): 272–82. http://dx.doi.org/10.1177/1077546316637979.

Full text
Abstract:
The bispectrum of rolling element bearing compound faults contains abundant fault characteristic information, and how to extract the fault feature effectively is a key problem. The fault diagnosis method of rolling element bearing compound faults based on Sparse No-Negative Matrix Factorization (SNMF)-Support Vector Data Description (SVDD) is proposed in the paper. The figure handling method SNMF is used firstly in fault feature extraction of the bispectrums of rolling element bearing different kinds of compound faults and the sparse coefficient matrices of the bispectrums are obtained. The sparse coefficient matrices are used as training and test input vectors of SVDD. At last, the three kinds of rolling element bearing compound faults (inner race outer race compound faults, outer race rolling element compound faults and inner race outer race rolling element compound faults) are classified correctly. In order to verify the advantages of the proposed method, the diagnosis results of the same three kinds of rolling element bearing compound faults based on No-Negative Matrix Factorization (NMF)-SVDD is used as comparison. The proposed method provides a new idea for fault diagnosis of rolling element bearing compound faults.
APA, Harvard, Vancouver, ISO, and other styles
4

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 (June 20, 2022): 214. http://dx.doi.org/10.18311/jmmf/2022/30060.

Full text
Abstract:
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 eight features in time domain were calculated for various bearing conditions like healthy, rolling element fault, outer and inner race fault. The feature extraction of vibration signal based on Empirical Mode Decomposition (EMD) is extensively explored and applied in diagnosis of fault in rolling bearings. This paper presents mathematical analysis for selection of valid Intrinsic Mode Functions (IMFs) of EMD. These chosen features are trained and classified using different classifiers. Among them K-star classifier is most reliable to categorize the bearing defects.
APA, Harvard, Vancouver, ISO, and other styles
5

Sun, J., Gang Yu, and Chang Ning Li. "Bearing Fault Diagnosis Using Gaussian Mixture Models (GMMs)." Applied Mechanics and Materials 10-12 (December 2007): 553–57. http://dx.doi.org/10.4028/www.scientific.net/amm.10-12.553.

Full text
Abstract:
This paper presents a novel method for bearing fault diagnosis based on wavelet transform and Gaussian mixture models (GMMs). Vibration signals for normal bearings, bearings with inner race faults, outer race faults and ball faults were acquired from a motor-driven experimental system. The wavelet transform was used to process the vibration signals and to generate feature vectors. GMMs were trained and used as a diagnostic classifier. Experimental results have shown that GMMs can reliably classify different fault conditions and have a better classification performance as compared to the multilayer perceptron neural networks.
APA, Harvard, Vancouver, ISO, and other styles
6

Chen, Xiaohui, Lei Xiao, Xinghui Zhang, and Zhenxiang Liu. "A heterogeneous fault diagnosis method for bearings in gearbox." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 229, no. 8 (July 27, 2014): 1491–99. http://dx.doi.org/10.1177/0954406214544727.

Full text
Abstract:
Bearing failure is one of the most important causes of breakdown of rotating machinery. These failures can lead to catastrophic disasters or result in costly downtime. One of the key problems in bearing fault diagnosis is to detect the bearing fault as early as possible. This capability enables the operator to have enough time to do some preventive maintenance. Most papers investigate the bearing faults under rational assumption that bearings work individually. However, bearings are usually working as a part of complex systems like a gearbox. The fault signal of bearings can be easily masked by other vibration generated from gears and shafts. The proposed method separates bearing signals from other signals, and then the optimum frequency band which the bearing fault signal is prominent is determined by mean envelope Kurtosis. Subsequently, the envelope analysis is used to detect the bearing faults. Finally, two bearing fault experiments are used to validate the proposed method. Each experiment contains two bearing fault modes, inner race fault and outer race fault. The results demonstrate that the proposed method can detect the bearing fault easier than spectral Kurtosis and envelope Kurtosis.
APA, Harvard, Vancouver, ISO, and other styles
7

Shi, Yuan Cheng, Yong Ying Jiang, Hai Feng Gao, and Jia Wei Xiang. "A Modified EEMD Decomposition for the Detection of Rolling Bearing Faults." Applied Mechanics and Materials 548-549 (April 2014): 369–73. http://dx.doi.org/10.4028/www.scientific.net/amm.548-549.369.

Full text
Abstract:
The vibration signals of rolling element bearings are non-linear and non-stationary and the corresponding fault features are difficult to be extracted. EEMD (Ensemble empirical mode decomposition) is effective to detect bearing faults. In the present investigation, MEEMD (Modified EEMD) is presented to diagnose the outer and inner race faults of bearings. The original vibration signals are analyzed using IMFs (intrinsic mode functions) extracted by MEEMD decomposition and Hilbert spectrum in the proposed method. The numerical and experimental results of the comparison between MEEMD and EEMD indicate that the proposed method is more effective to extract the fault features of outer and inner race of bearings than EEMD.
APA, Harvard, Vancouver, ISO, and other styles
8

Tian, Jing, Yan-Ting Ai, Cheng-Wei Fei, Feng-Ling Zhang, and Yat-Sze Choy. "Dynamic modeling and simulation of inter-shaft bearings with localized defects excited by time-varying displacement." Journal of Vibration and Control 25, no. 8 (January 29, 2019): 1436–46. http://dx.doi.org/10.1177/1077546318824927.

Full text
Abstract:
To accurately describe the dynamic features of inter-shaft bearings with localized defect under operation, the dynamic model of inter-shaft bearing with localized defects was established with respect to time-varying displacement excitation. Based on fault simulations on a birotor experimental rig, the developed dynamic model of inter-shaft bearing is validated to have high accuracy (over 99%) when localized defects happen on inner and outer race with co- and counter-rotation, which indicates that the model can be adopted to simulate the faults of inter-shaft bearing instead of experiment. Through investigation of the square-root (SR) amplitudes of bearing vibration with different defect sizes, radial loads, and rotational directions, we find that the SR amplitudes of bearing vibration increase with increasing defect size and radial load for both co- and counter-rotation. The amplitudes of counter-rotation are larger than those of co-rotation for inner race and outer race, and the amplitude of inner race defect are larger than that of outer race defect for the same defect size or same radial load. This work reveals the SR variation of bearing vibration with localized surface defects under different defect sizes and radial loads, and accurately describes the dynamic characteristics of inter-shaft bearing with localized defects. The efforts of this study open a door to adopt a dynamic model in the future to evaluate and monitor the health condition of inter-shaft bearings in an aeroengine or other rotating machinery.
APA, Harvard, Vancouver, ISO, and other styles
9

Du, Jianxi, Lingli Cui, Jianyu Zhang, Jin Li, and Jinfeng Huang. "The Method of Quantitative Trend Diagnosis of Rolling Bearing Fault Based on Protrugram and Lempel–Ziv." Shock and Vibration 2018 (November 1, 2018): 1–8. http://dx.doi.org/10.1155/2018/4303109.

Full text
Abstract:
This paper proposes a new method to realize the quantitative trend diagnosis of bearings based on Protrugram and Lempel–Ziv. Firstly, the fault features of original fault signals of bearing inner and outer race with different severity are extracted using Protrugram algorithm, and the optimal analysis frequency band is selected which reflects the fault characteristic. Then, the Lempel–Ziv complexity of the frequency band is calculated. Finally, the relationship between Lempel–Ziv complexity and fault size is obtained. Analysis results show that the severity of fault is proportional to the Lempel–Ziv complexity index value under different fault types. The Lempel–Ziv complexity showed different trend rules, respectively, in the inner and outer race, which realized the quantitative trend diagnosis of bearing faults.
APA, Harvard, Vancouver, ISO, and other styles
10

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 (June 30, 2022): 77–90. http://dx.doi.org/10.20855/ijav.2022.27.21829.

Full text
Abstract:
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 element defects using conventional techniques in past decades. However, Machine Learning (ML) has emerged as another way of bearing fault diagnosis. In this work, fault signatures of ball bearings are classified using a total of 6 (with 24 subcategories) ML models, and a comparative performance of these models is presented. The ML classifiers are trained with extracted time-domain and frequency-domain features using open-source Case Western Reserve University (CWRU) bearing data. Two datasets of different sample size and number of samples of vibration data corresponding to a healthy ball bearing, a defective bearing with inner race defect, a ball defect, and an outer race defect, running at a particular set of working conditions, are considered. The accuracy of ML models is compared to identify the best model for classifying the faults under consideration.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Inner race bearing fault"

1

Spagnol, Marco. "Maintenance of electrical machines: Instantaneous Angular Speed analysis." Doctoral thesis, Università degli studi di Trieste, 2015. http://hdl.handle.net/10077/11102.

Full text
Abstract:
2013/2014
This research is focused on the condition monitoring of electrical machines and its long term purpose is to monitor electrical and mechanical faults at the same time, in non-stationary conditions (variable load and speed), with a single piece of hardware. The Instantaneous Angular Speed (IAS) measurement of an electrical machine is proposed and analysed in order to detect the fault development inside it. Chapter 1 introduces some basic principles about the maintenance of an electrical machine. Machine unscheduled downtimes are frequently caused by bearing faults, and rotor/stator faults. Monitoring systems are needed when the machine is very important for the plant (cost, safety). In this chapter, the electrical machine’s behaviour is also examined. Induction electrical machines have been chosen for this research. A review of the excitation frequencies is reported in the chapter. In the last section, characteristic fault frequencies (from mechanical and electrical sources) are collected. Chapter 2 presents the IAS measurement and its signal processing. The IAS is the measurement of the shaft rotating speed in order to visualize what’s happening during a single or in multiple turns. There are many measurement methods which are based either analogical to digital conversion or which use counters. Analogical to digital methods use a standard data acquisition board. Counter methods have to use specific hardware that is more expensive, but with less data to store. In this research, the counter method is used, combined with the Elapsed Time (ET) counting technique. Chapter 3 describes the encoder system. Its output signal is acquired with an oscilloscope and with the counter board. The signal’s differences are highlighted. In this chapter, the measurement’s source of errors are listed: the encoder’s geometrical error, the counter’s quantization error, the clock stability and the general electrical noise. Chapter 4 collects all the experimental tests done during the PhD research. Three experimental test rigs are shown and two measurements at Nidec ASI S.p.A. are reported. Note that the experimental test rigs were designed and built at the Università degli Studi di Trieste during the three years of the PhD. Experimental Test Rig 1 (ETR1) is used to understand the electrical motor’s behaviour with varying speed, the difference between the IAS and the speed acquired with the Torsional Laser Vibrometer, the difference between the IAS and the acceleration signal measured with an accelerometer located on the motor’s stator, the effect of the unbalance in the IAS measurement. Experimental Test Rig 2 (ETR2) allows to examine the load effect on the IAS measurement, the magneto-motive force harmonics, the slip and the rotor effects. Experimental Test Rig 3 (ETR3) is designed in order to detect the Inner Race Bearing Fault (Ball Pass Frequency Inner - BPFI) with varying load. The acceleration, the voltage and the current are compared with the Instantaneous Angular Speed. The motor is also tested with an unbalanced power supply. The two measurements at Nidec ASI S.p.A show how the IAS measurement could be implemented in an industrial machine larger than the one tested in the laboratory. This research presents the pros and cons of the IAS measurement, highlighting the capability of detecting BPFI bearing fault, feeling the load variations owing to the brake system (a synchronous generator), measuring the Fundamental Train Frequency of an healthy bearing, detecting unbalance in the rotor and other special features. The author would like to thank the Fondo Sociale Europeo, the Regione Friuli Venezia Giulia and Nidec ASI S.p.A (an electrical motor company) for the sponsorship and the collaboration during the three PhD years covered by the SHARM project ”Manutenzione Preventiva Integrata”.
Questo studio è focalizzato al monitoraggio dello stato di salute delle macchine elettriche con l'obbiettivo finale di monitorare danni meccanici ed elettrici, in condizioni non stazionarie (carico e velocità variabili), con un singolo sistema hardware. Viene quindi proposta ed analizzata la misura della Velocità Angolare Istantanea (Instantaneous Angular Speed - IAS) di una macchina elettrica allo scopo di prevedere l'insorgere di guasti al suo interno. Il Capitolo 1 introduce i principi base relativi alla manutenzione di macchine elettriche. Di frequente, le fermate non programmate sono conseguenti a danni su cuscinetti e su rotore/statore. I sistemi di monitoraggio sono indispensabili quando la macchina è molto importante nel contesto dell'impianto, considerazione esaminata sia dal punto di vista del costo che della sicurezza. In questo capitolo, viene analizzato anche il funzionamento della macchina elettrica. Dopo un'attenta valutazione, per lo sviluppo di questa ricerca sono state selezionate le macchine ad induzione asincrone. Nel capitolo è riportata anche un'analisi bibliografica sulle frequenze caratteristiche delle forzanti elettromagnetiche presenti. Nell'ultima sezione vengono elencate le frequenze tipiche dei danni rilevabili in misure di tipo vibrazionale ed elettrico. Il Capitolo 2 presenta la misura IAS. Questa rappresenta la misurazione della velocità di rotazione dell'albero e viene analizzata con accuratezza, individuando la relazione tra velocità di rotazione e le caratteristiche dell'encoder; inoltre vengono descritti i vari processamenti del segnale. Tale sistema permette di visualizzare ciò che sta accadendo alla macchina durante il suo funzionamento, in una o più rotazioni. Esistono metodi di misura basati o sulla conversione analogico-digitale o che prevedono l’impiego di contatori. I primi si servono di una scheda di acquisizione dati standard, mentre i secondi richiedono l'utilizzo di un hardware specifico, che alle volte può risultare più costoso, ma permette di acquisire i dati occupando una quantità inferiore di memoria. In questa tesi si è scelto di utilizzare un contatore per eseguire la misura IAS, sfruttando il conteggio Elapsed Time (ET). Il Capitolo 3 descrive l'encoder. Il segnale in uscita dal dispositivo viene acquisito con una scheda contatore e con un oscilloscopio in modo da confrontare ed analizzare le differenze presenti. In questo capitolo vengono elencate le tipologie di errore presenti nel sistema encoder: l'errore geometrico, l'errore di quantizzazione, l'errore dovuto alla stabilità del clock interno e l'errore dovuto a fonti esterni di rumore elettrico. Il Capitolo 4 raccoglie tutti i test sperimentali condotti durante il dottorato. Sono stati progettati e costruiti tre setup allo scopo di evidenziare particolari aspetti e problematiche; sono riportate anche due misure eseguite presso la sala prove dell'azienda Nidec ASI S.p.A. Il setup Experimental Test Rig 1 (ETR1) è stato utilizzato per conseguire le seguenti finalità: capire il funzionamento del motore elettrico con velocità variabile, analizzare la differenza della velocità acquisita con un torsiometro laser ed una scheda contatore, confrontare una misura vibrazionale (accelerometro posizionato sullo statore del motore) e la misura IAS, analizzare l'effetto dello sbilanciamento sulla misura IAS. Il setup Experimental Test Rig 2 (ETR2) permette di esaminare l'effetto del carico sulla misura IAS, le armoniche della forza elettromotrice, l'effetto dello slip e del rotore. Il setup Experimental Test Rig 3 (ETR3) è progettato in modo da evidenziare un difetto sulla guida interna di un cuscinetto, considerando anche un carico variabile. L'accelerazione, il voltaggio e la corrente sono confrontate con la Velocità Angolare Istantanea. Il motore viene testato anche applicando una tensione di alimentazione sbilanciata. Le due misure rilevate in Nidec ASI S.p.A dimostrano che la misura IAS può essere implementata in macchine industriali di grandi dimensioni e non solo nei setup di laboratorio. Questa ricerca espone gli aspetti positivi e negativi della misura IAS, evidenziando le capacità di individuare un danno sulla guida interna di un cuscinetto, captare le variazioni di carico prodotte dal freno (un generatore sincrono), misurare la Fundamental Train Frequency di un cuscinetto in buona salute, individuare uno sbilanciamento ed altre funzionalità. L'autore vuole ringraziare il Fondo Sociale Europeo, la Regione Friuli Venezia Giulia e l'azienda Nidec ASI S.p.A (produttore di macchine elettriche di medio-grandi dimensioni) per la sponsorizzazione e la collaborazione durante i tre anni di dottorato previsti dal progetto SHARM ”Manutenzione Preventiva Integrata”.
XXVII Ciclo
1983
APA, Harvard, Vancouver, ISO, and other styles
2

Yu-LingChen and 陳聿翎. "Nonlinear Dynamic Analysis of Rotor-Ball Bearing System due to Surface Waviness of Inner and Outer Race." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/696653.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Books on the topic "Inner race bearing fault"

1

Effect of speed and press fit on fatigue life of roller-bearing inner-race contact. [Washington, D.C.]: National Aeronautics and Space Administration, Scientific and Technical Information Branch, 1985.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

Effect of speed and press fit on fatigue life of roller-bearing inner-race contact. [Washington, D.C.]: National Aeronautics and Space Administration, Scientific and Technical Information Branch, 1985.

Find full text
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Inner race bearing fault"

1

Ghods, Amirhossein, and Hong-Hee Lee. "Monte Carlo-Discrete Wavelet Transform for Diagnosis of Inner/Outer Race Bearings Faults in Induction Motors." In Intelligent Computing Theory, 630–36. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09333-8_68.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Luo, Zeyu, Xian-Bo Wang, and Zhi-Xin Yang. "Fault Representations of Bearing Race Based on Grayscale Maps and CNN Networks." In Proceedings of IncoME-V & CEPE Net-2020, 61–68. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75793-9_7.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Belkacemi, Bellal, and Salah Saad. "Inner and Outer Race Bearing Defects of Induction Motor Running at Low Speeds Signal Analysis with DWT." In Advances in Intelligent Systems and Computing, 975–83. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73689-7_92.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Chetverikov, B. S., N. N. Slavkova, A. N. Unkovskiy, and M. S. Babkin. "Modeling of the Projection Control Roundness Raceway of the Inner Ring Race of a Ball Bearing Support." In Lecture Notes in Civil Engineering, 131–37. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75182-1_18.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Gangavva C, J. Alamelu Mangai, and Mohit Bansal. "An Investigation of Ensemble Learning Algorithms for Fault Diagnosis of Roller Bearing." In Advances in Parallel Computing Algorithms, Tools and Paradigms. IOS Press, 2022. http://dx.doi.org/10.3233/apc220016.

Full text
Abstract:
Roller Bearing (RB) is one of the critical mechanical components in rotating machineries. Failure of a bearing may cause the fatal breakdown of an entire machine and inestimable financial losses due to its continuous rotation. Hence, it is significant to diagnose the fault accurately at an early stage so that it helps in predictive maintenance of the machine from malfunctioning. In the recent developments, Machine Learning (ML) has shown a drastic change in the way we predict, analyze and interpret the results. In this paper, a diagnostic technique is being proposed to identify the bearing faults that employs ensemble learning algorithms such as Bagging, Extra Tree and Gradient Boosting classifiers. The proposed method includes 1) Pre-processing of vibration data 2) Extracting statistical features such as Mean, Standard Deviation, Kurtosis, Crest Factor and Mel-Frequency Cepstral Co-efficient (MFCC) features and 3) Training the Ensemble Learning algorithms for classifying the various faults based on extracted features. For experimentation, vibration data is collected from the Case Western Reserve University (CWRU) Laboratory to diagnose 12 different fault types associated with Inner Race (IR), Outer Race (OR), Ball fault and normal bearing of varying diameters. Results shows that Ensemble learning algorithms performs better based on MFCC features as compared to statistical features.
APA, Harvard, Vancouver, ISO, and other styles
6

Trivedi, Shrey, D. V. Patel, V. M. Bhojawala, and K. M. Patel. "Identification of faulty condition of rolling element bearing with inner race defect using time and frequency domain parameters of vibration signature." In Technology Drivers: Engine for Growth, 223–29. CRC Press, 2018. http://dx.doi.org/10.1201/9780203713143-34.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

"Industrial Case Histories – VSA Detected Inner and Outer Race Faults in Rolling Element Bearings in SCIMS." In Vibration Monitoring of Induction Motors, 103–26. Cambridge University Press, 2020. http://dx.doi.org/10.1017/9781108784887.007.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Gao, Shang, and Cuicui Du. "Multi-Channel IoT-Based Ensemble-Features Fault Diagnosis for Machine Condition Monitoring." In Studies in Applied Electromagnetics and Mechanics. IOS Press, 2020. http://dx.doi.org/10.3233/saem200037.

Full text
Abstract:
This paper proposes a multi-channel internet of things (IoT)-based industrial wireless sensor network (IWSN) with ensemble-features fault diagnosis for machine condition monitoring and fault diagnosis. In this paper, the rolling bearing is taken as an example of monitored industrial equipment due to its wide use in industrial processes. The rolling bearing vibration signals are measured for further processing and analysis. On-sensor node ensemble feature extraction and fault diagnosis using Back Propagation network are then investigated to address the tension between the higher system requirements of IWSNs and the resource-constrained characteristics of sensor nodes. A two-step classifier fusion approach using Dempster-Shafer theory is also explored to increase diagnosis result quality. Four rolling bearing operating in cage fracture, rolling ball spalling, inner ring spalling and outer ring spalling are monitored to evaluate the proposed system. The final fault diagnosis results using the proposed classifier fusion approach give a result certainty of at least 94.21%, proving the feasibility of the proposed method to identify the bearing-fault patterns. This paper is conducted to provide new insights into how a high-accuracy IoT-based ensemble-features fault diagnosis algorithm is designed and further giving advisable reference to more IWSNs scenarios.
APA, Harvard, Vancouver, ISO, and other styles
9

Feng, Yuxiang, Jinyu Yao, Xiaohan Sun, and Cunqing Yuan. "Study on Lubrication Flow and Heat Transfer Characteristics of Under-Race Lubrication for High Speed Ball Bearing." In Advances in Transdisciplinary Engineering. IOS Press, 2022. http://dx.doi.org/10.3233/atde220022.

Full text
Abstract:
In order to reveal the lubrication characteristics of high-speed ball bearings under the sub-ring lubrication mode and improve the lubrication efficiency of ball bearings, a three-dimensional geometric model of high-speed ball bearings was established. By using multiple reference frame and VOF model, the two-phase flow field and temperature field of inner and outer ring in the bearing under different conditions were investigated, and the correlation law of lubricating oil flow and heat transfer was obtained. At the same time, the definition of the penetration rate was given quantitatively for the lubrication efficiency of the outer ring. The lubrication efficiency of ball bearings under-race lubrication was evaluated from the aspects of rotation speed and penetration rate. The results showed that the oil and gas distribution and temperature distribution in the lubrication under the ring are not uniform, and there was an obvious correlation between the two; the increase of speed reduced the oil volume fraction in the bearing. The oil distribution was more uniform which was useful for reducing the temperature difference between the inner and outer rings.
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Inner race bearing fault"

1

Stack, J. R., T. G. Habetler, and R. G. Harley. "Fault signature modeling and detection of inner race bearing faults." In International Electric Machines and Drives Conference. IEEE, 2005. http://dx.doi.org/10.1109/iemdc.2005.195734.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Tian, Jing, Yanting Ai, Ming Zhao, Chengwei Fei, and Fengling Zhang. "Fault Diagnosis Method for Inter-Shaft Bearings Based on Information Exergy and Random Forest." In ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/gt2018-76101.

Full text
Abstract:
To reasonably process the complex signals and improve the diagnosis accuracy of inter-shaft bearing incipient faults, this paper develops wavelet energy spectrum exergy (WESE) and random forest (RF) (short for WESE-RF) method with respect to acoustic emission (AE) signals. Inter-shaft bearing faults, which contain inner race fault, outer race fault, rolling element faults and normal status under different measuring points and different rotational speeds, are simulated based on the test rig of inter-shaft bearings, to collect the AE signals of these faults. Regarding the AE signals of inter-shaft bearing faults, the WESE values, one signal feature, are extracted from an information exergy perspective, and are applied to structure feature vectors. The WESE values of these AE signals are regarded as the sample set which include the training samples subset used to establish the WESE-RF model of fault diagnosis and the test samples subset applied to test the effectiveness of the developed WESE-RF model. The investigation on the fault diagnosis of inter-shaft bearing demonstrates the fault diagnosis method with the WESE-RF has good generalization ability and high diagnostic accuracy of over 0.9 for inter-shaft bearing fault. The efforts of this paper provide a useful approach-based information exergy and wavelet energy spectrum for inter-shaft bearing fault diagnosis.
APA, Harvard, Vancouver, ISO, and other styles
3

Muruganatham, Bubathi, M. A. Sanjith, B. Krishna Kumar, S. A. V. Satya Murty, and P. Swaminathan. "Inner race bearing fault detection using Singular Spectrum Analysis." In 2010 IEEE International Conference on Communication Control and Computing Technologies (ICCCCT). IEEE, 2010. http://dx.doi.org/10.1109/icccct.2010.5670774.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Wang, Dong, Qiang Miao, Rui Sun, and Hong-Zhong Huang. "Bearing Fault Diagnosis Using Singular Value Decomposition and Hidden Markov Modeling." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-86471.

Full text
Abstract:
Condition monitoring and fault diagnosis of bearings are of practical significance in industry. In order to get a feature containing different fault signatures, this paper uses Wavelet Transform (WT), Wavelet Lifting Scheme (WLS) and Empirical Mode Decomposition (EMD), respectively, to decompose signal into different frequency bands. Then, Singular Value Decomposition (SVD) is utilized to extract intrinsic characteristic of signal from obtained matrix. These singular value vectors are regarded as inputs to Hidden Markov Models (HMM) for identification of machinery health condition. In this research, the fault diagnosis system is validated by motor bearing data, including normal bearings, inner race fault bearings, outer race fault bearings and roller fault bearings. Analysis results show that this method is effective in bearing fault diagnosis and its classification rate is excellent.
APA, Harvard, Vancouver, ISO, and other styles
5

Haj Mohamad, T., C. A. Kitio Kwuimy, and C. Nataraj. "Discrimination of Multiple Faults in Bearings Using Density-Based Orthogonal Functions of the Time Response." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-68375.

Full text
Abstract:
This study investigates the use of the mapped density of time response using orthogonal functions to detect single and multiple faults in rolling element bearings. The method is based on constructing the density of a single time response of the system by using orthogonal functions. The coefficients of the orthogonal functions create the feature vector in order to discriminate between different rolling element bearing faults. The method does not require preprocessing of the data, noise reduction, or feature selection. This method has been applied to vibration data of different bearing conditions at rotational speeds ranging from 300 rpm to 3000 rpm. These conditions include a healthy bearing, and bearings with defects in inner race, outer race, combination of inner race and outer race and rolling element. The results have shown remarkable detection efficiency in the case of a single and two bearing fault configurations. In general, for all bearing configurations, the approach has high performance in detecting defective conditions. These results indicate that using the mapped density to characterize the system under different conditions has considerable potential in bearing diagnostics.
APA, Harvard, Vancouver, ISO, and other styles
6

Huang, Huan, Natalie Baddour, and Ming Liang. "Algorithm for Multiple Time-Frequency Curve Extraction From Time-Frequency Representation of Vibration Signals for Bearing Fault Diagnosis Under Time-Varying Speed Conditions." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-67171.

Full text
Abstract:
Bearing fault diagnosis under constant operational condition has been widely investigated. Monitoring the bearing vibration signal in the frequency domain is an effective approach to diagnose a bearing fault since each fault type has a specific Fault Characteristic Frequency (FCF). However, in real applications, bearings are often running under time-varying speed conditions which makes the signal non-stationary and the FCF time-varying. Order tracking is a commonly used method to resample the non-stationary signal to a stationary signal. However, the accuracy of order tracking is affected by many factors such as the precision of the measured shaft rotating speed and the interpolation methods used. Therefore, resampling-free methods are of interest for bearing fault diagnosis under time-varying speed conditions. With the development of Time-Frequency Representation (TFR) techniques, such as the Short-Time Fourier Transform (STFT) and wavelet transform, bearing fault characteristics can be shown in the time-frequency domain. However, for bearing fault diagnosis, instantaneous time-frequency characteristics, i.e. Time-Frequency (T-F) curves, have to be extracted from the TFR. In this paper, an algorithm for multiple T-F curve extraction is proposed based on a path-optimization approach to extract T-F curves from the TFR of the bearing vibration signal. The bearing fault can be diagnosed by matching the curves to the Instantaneous Fault Characteristic Frequency (IFCF) and its harmonics. The effectiveness of the proposed algorithm is validated by experimental data collected from a faulty bearing with an outer race fault and a faulty bearing with an inner race fault, respectively.
APA, Harvard, Vancouver, ISO, and other styles
7

Nakhaeinejad, Mohsen, Jaewon Choi, and Michael D. Bryant. "Nonlinear Mechanics of Rolling Contacts With Surface Defects." In STLE/ASME 2010 International Joint Tribology Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/ijtc2010-41246.

Full text
Abstract:
Nonlinear behavior of force and displacements in rolling contacts with the presence of surface defects are studied. Model-based fault assessments in rolling element bearings and gears require detailed modeling and dynamics of faults. A detailed model of rolling element bearings with direct correspondence between parameters of the model and physical components is developed. The model incorporates dynamics of faults, nonlinear contacts, slips and surface separations. Mechanics of contacts with inner race faults (IRF), ball faults (BF), and outer race faults (ORF) are studied using the developed model. Contacts force, displacement and impulse signals are studied for different size and types of surface defects. It is shown that impulse signals contain useful information about the severity of surface defects in rolling element bearing. Results provide model-based diagnostics a deep knowledge of rolling contact mechanics with surface defects to be used for fault assessments.
APA, Harvard, Vancouver, ISO, and other styles
8

Bhatnagar, Aashish, P. K. Kankar, Satish C. Sharma, and S. P. Harsha. "ANN Based Fault Classification of High Speed Ball Bearings." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-87016.

Full text
Abstract:
In the rotating machines, maintenance of the high speed operated bearings is the major problem and is one of the key issues due to excessive vibrations. Hence, the vibration signatures can be used as a feature for the fault diagnosis. This paper presents the Artificial Neural Networks (ANN) based fault analysis, which is used to classify various known faults using the features extracted from the vibration signals. The vibration signals from the piezoelectric accelerometers are being measured for the following conditions — No defect (NOD), Outer race defect (ORD), Inner race defect (IRD), Ball fault (BF) and Combination of above (COMB). The features are extracted from the time domain using the statistical method. These features are filtered using wavelet filter & kernel filter and compiled as the input vectors. The multilayer neural network is trained by these input vectors. The training and testing results show that wavelet and kernel filter can be effective tool in the diagnosis of ball bearing faults using ANN. Results obtained from the ANN predict that the wavelet filter provides good accuracy with reduction in the training time.
APA, Harvard, Vancouver, ISO, and other styles
9

Abu-Mahfouz, Issam, and Amit Banerjee. "Bearing Fault Parameter Identification Under Varying Operating Conditions Using Vibration Signals and Evolutionary Algorithms." In ASME 2014 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/imece2014-39124.

Full text
Abstract:
This paper presents an effective bearing fault parameter identification scheme based on evolutionary optimization techniques. Three seeded faults in the rotating machinery supported by the test roller bearing include inner race fault, outer race fault and a single ball defect. The fault related features are extracted experimentally by processing the acquired vibration signals in both the time and frequency domain. Techniques based on the power spectral density (PSD) and wavelet transform (WT) are utilized for feature extraction. The sensitivity of the proposed method is investigated under varying operating speeds and radial bearing load. In this study, the inverse problem of parameter identification is investigated. The problem of parameter identification is recast as an optimization problem and two well known evolutionary algorithms, differential evolution (DE) and particle swarm optimization (PSO), are used to identify system parameters given a system response. For online parameter identification, differential evolution outperforms particle both in terms of adaptability and tighter convergence properties. The distinction between the two methods is not distinctively obvious on the offline parameter identification problem.
APA, Harvard, Vancouver, ISO, and other styles
10

Wang, Fengli, and Hua Chen. "Degradation Feature Extraction of Rolling Bearings Based on Optimal Ensemble Empirical Mode Decomposition and Improved Composite Spectrum Analysis." In ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/gt2018-75041.

Full text
Abstract:
Rolling bearing is a key part of turbomachinery. The performance and reliability of the bearing is vital to the safe operation of turbomachinery. Therefore, degradation feature extraction of rolling bearing is important to prevent it from failure. During rolling bearing degradation, machine vibration can increase, and this may be used to predict the degradation. The vibration signals are however complicated and nonlinear, making it difficult to extract degradation features effectively. Here, a novel degradation feature extraction method based on optimal ensemble empirical mode decomposition (EEMD) and improved composite spectrum (CS) analysis is proposed. Firstly, because only a few IMFs are expected to contain the information related to bearing fault, EEMD is utilized to pre-process the vibration signals. An optimization method is designed for adaptively determining the appropriate EEMD parameters for the signal, so that the significant feature components of the faulty bearing can be extracted from the signal and separated from background noise and other irrelevant components to bearing faults. Then, Bayesian information criterion (BIC) and correlation kurtosis (CK) are employed to select the sensitive intrinsic mode function (IMF) components and obtain fault information effectively. Finally, an improved CS analysis algorithm is used to fuse the selected sensitive IMF components, and the CS entropy (CSE) is extracted as degradation feature. Experimental data on the test bearings with single point faults separately at the inner race and rolling element were studied to demonstrate the capabilities of the proposed method. The results show that it can assess the bearing degradation status and has good sensitivity and good consistency to the process of bearing degradation.
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Inner race bearing fault' for particular source types:
Journal articles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography