Diagnosis and Monitoring Method for Detecting and Localizing Bearing Faults

Saida Dahmane, Fouad Berrabah, Mabrouk Defdaf, Saad Salah


Induction motors in modern industry are becoming more and more functional and complex. Unfortunately, these machines are not free from damages what make their fault diagnosis the most critical aspect of system monitoring and maintenance. Vibrational signal data yields relevant information about the state of the entire system, as well as specifically about one of its components that makes its analysis quite interesting. For this effect, the current paper aims to propose an automatic diagnosis and monitoring method for detecting and locating bearing faults in an induction motor based on vibration signal processing. The suggested method combines the discrete wavelet transform (DWT) with the envelope spectrum (ENV) as advanced signal processing, incorporating a machine learning algorithm based on random forest classifier. The discrete wavelet transforms (DWT), using the Haar wavelet, decomposes the vibrational signal to provide both approximations and details. Each detail is then reconstructed to avoid any missing of information. To precisely select the reconstructed detail (𝑅𝑒𝑐𝑑𝑘) that provides pertinent information about bearing faults, a statistical study is conducted. This study involves calculating four indicators (Root mean square (RMS), correlation coefficient (CC), energy coefficient (EC) and peak to peak (P2P) factor) is performed for each (𝑅𝑒𝑐𝑑𝑘). These indicators are compared with threshold indicators, and this criterion is met by the reconstructed details 1 and 3. The obtained reconstructed details are then subjected to the spectral envelope analysis to detect the fault frequencies, which are considered as new features entering the random forest classifier model. This combination of approaches allows better feature extraction and structuring of the dataset, leading to improved accuracy of the random forest classifier, achieving a higher classification rate of more than 99,53 %. The proposed DWT-ENV-RF method indicates well its efficiency when compared to other recent works, and the attained results are all confirmed by the experimental tests conducted in the CWRU laboratory.


Induction motor; Bearing fault; DWT; Spectral ENV; Random Forest


C. Lessmeier, J. K. Kimotho, D. Zimmer, and W. Sextro, “Condition monitoring of bearing damage in electromechanical drive systems by using motor current signals of electric motors: a benchmark data set for data-driven classification.,” Third Eur. Conf. Progn. Heal. Manag. Soc. 2016, no. Cm, pp. 152–156, 2016.

R. A. Rahman, M. F. Erikyatna, and A. F. Hery, “Study on Predictive Maintenance of V-Belt in Milling Machines Using Machine Learning Study on Predictive Maintenance of V-Belt in Milling Machines Using Machine Learning,” no. November, 2022, doi: 10.17977/um016v6i22022p085.

A. Picot, Z. Obeid, J. Régnier, S. Poignant, O. Darnis, and P. Maussion, “Statistic-based spectral indicator for bearing fault detection in permanent-magnet synchronous machines using the stator current,” Mech. Syst. Signal Process., vol. 46, no. 2, pp. 424–441, 2014, doi: 10.1016/j.ymssp.2014.01.006.

M. Ye, J. Zhang, and J. Yang, “Bearing Fault Diagnosis under Time-Varying Speed and Load Conditions via Observer-Based Load Torque Analysis,” Energies, vol. 15, no. 10, 2022, doi: 10.3390/en15103532.

A. Allouche et al., “A PLL based mechanical faults detection in PMSM at variable speed,” vol. 51, no. 24, pp. 1445–1451, 2018, doi: 10.1016/j.ifacol.2018.09.534.

A. D. Nembhard, J. K. Sinha, A. J. Pinkerton, and K. Elbhbah, “Fault diagnosis of rotating machines using vibration and bearing temperature measurements,” Diagnostyka, vol. 14, no. 3, pp. 45–51, 2013.

M. A. Jamil and S. Khanam, “Fault Classification of Rolling Element Bearing in Machine Learning Domain,” Int. J. Acoust. Vib., vol. 27, no. 2, pp. 77–90, 2022, doi: 10.20855/ijav.2022.27.21829.

R. N. Toma and J. M. Kim, “bearing fault classification of induction motors using discrete wavelet transform and ensemble machine learning algorithms,” Appl. Sci., vol. 10, no. 15, 2020, doi: 10.3390/APP10155251.

G. Georgoulas, T. Loutas, C. D. Stylios, and V. Kostopoulos, “Bearing fault detection based on hybrid ensemble detector and empirical mode decomposition,” Mech. Syst. Signal Process., vol. 41, no. 1–2, pp. 510–525, 2013, doi: 10.1016/j.ymssp.2013.02.020.

P. Thanh Noi and M. Kappas, “Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery,” Sensors (Basel)., vol. 18, no. 1, 2017, doi: 10.3390/s18010018.

S. M. Yadavar Nikravesh, H. Rezaie, M. Kilpatrik, and H. Taheri, “Intelligent fault diagnosis of bearings based on energy levels in frequency bands using wavelet and support vector machines (SVM),” J. Manuf. Mater. Process., vol. 3, no. 1, 2019, doi: 10.3390/jmmp3010011.

P. Kamat et al., “Bearing Fault Detection Using Comparative Analysis of Random Forest, ANN, and Autoencoder Methods,” Lect. Notes Networks Syst., vol. 204, no. June, pp. 157–171, 2021, doi: 10.1007/978-981-16-1089-9_14.

D. Wu et al., “An automatic bearing fault diagnosis method based on characteristics frequency ratio,” Sensors (Switzerland), vol. 20, no. 5, pp. 1–12, 2020, doi: 10.3390/s20051519.

S. Adamczak and W. Makieła, “Analyzing variations in roundness profile parameters during the wavelet decomposition process using the MATLAB environment,” Metrol. Meas. Syst., vol. 18, no. 1, pp. 25–34, 2011, doi: 10.2478/v10178-011-0003-6.

L. Souad, B. Azzedine, C. B. D. Eddine, B. Boualem, M. Samir, and M. Youcef, “Induction machine rotor and stator faults detection by applying the DTW and N-F network,” Proc. IEEE Int. Conf. Ind. Technol., vol. 2018-Febru, no. February, pp. 431–436, 2018, doi: 10.1109/ICIT.2018.8352216.

C. Uyulan and T. Erguzel, “Comparison of Wavelet Families for Mental Task Classification,” J. Neurobehav. Sci., vol. 3, no. 2, p. 59, 2016, doi: 10.5455/jnbs.1454666348.

S. Seninete, M. Mimi, B. D. Eddine Cherif, and A. Ould Ali, “Vibration Signal Analysis for Bearing Fault Diagnostic of Asynchronous Motor using HT-DWT Technique,” Proc. - 2019 6th Int. Conf. Image Signal Process. their Appl. ISPA 2019, no. February 2020, 2019, doi: 10.1109/ISPA48434.2019.8966801.

A. A. Bengharbi, S. Laribi, T. Allaoui, and A. Mimouni, “Photovoltaic system faults diagnosis using discrete wavelet transform based artificial neural networks,” Electr. Eng. Electromechanics, vol. 2022,no. 6, pp. 42–47, 2022, doi: 10.20998/2074-272X.2022.6.07.

A. Rabah and K. Abdelhafid, “2777 . Rolling bearing fault diagnosis based on improved complete ensemble empirical mode of decomposition with adaptive noise combined with minimum entropy deconvolution,” pp. 240–257, 2018, doi: 10.21595/jve.2017.18762.

N. Bessous, S. E. Zouzou, W. Bentrah, S. Sbaa, and M. Sahraoui, “Diagnosis of bearing defects in induction motors using discrete wavelet transform,” Int. J. Syst. Assur. Eng. Manag., vol. 9, no. 2, pp. 335–343, 2018, doi: 10.1007/s13198-016-0459-6.

B. D. Eddine Cherif, S. Seninete, and M. Defdaf, “a Novel, Machine Learning-Based Feature Extraction Method for Detecting and Localizing Bearing Component Defects,” Metrol. Meas. Syst., vol. 29, no. 2, pp. 333–346, 2022, doi: 10.24425/mms.2022.140038.

R. Costache et al., “Flash-flood susceptibility assessment using multi-criteria decision making and machine learning supported by remote sensing and GIS techniques,” Remote Sens., vol. 12, no. 1, 2020, doi: 10.3390/RS12010106.

Bearing Data Center - Case Western Reserve university, “http://csegroups.case.edu/bearingdatacenter/ pages/welcome-case western-reserve-university.”

J. Tian, L. Liu, F. Zhang, Y. Ai, R. Wang, and C. Fei, “Multi-domain entropy-random forest method for the fusion diagnosis of inter-shaft bearing faults with acoustic emission signals,” Entropy, vol. 22, no. 1, p. 57, 2020, doi: 10.3390/e22010057.

L. Yuan, D. Lian, X. Kang, Y. Chen, and K. Zhai, “Rolling Bearing Fault Diagnosis Based on Convolutional Neural Network and Support Vector Machine,” IEEE Access, vol. 8, pp. 137395–137406, 2020, doi: 10.1109/ACCESS.2020.3012053.

X. Li, W. Zhang, and Q. Ding, “Cross-domain fault diagnosis of rolling element bearings using deep generative neural networks,” IEEE Trans. Ind. Electron., vol. 66, no. 7, pp. 5525–5534, 2019, doi: 10.1109/TIE.2018.2868023.

Full Text: PDF


  • There are currently no refbacks.


Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
ISSN 2089-3272

Creative Commons Licence

This work is licensed under a Creative Commons Attribution 4.0 International License.

web analytics
View IJEEI Stats