Artificial Neural Network and ESPRIT-TLS Combination-Based Approach for Fault Bearing Recognition in Induction Machines
Main Article Content
Abstract
Several studies have been conducted in recent decades on the detection of bearing faults to develop methods for real-time monitoring. Faults are found in almost all electromechanical systems and the major cause of damage to machinery, resulting in serious accidents to humans and material environments. While methods such as MCSA (Motor Current Signature Analysis) and PCA (Principal Component Analysis) have been widely used for obtaining and analyzing the machine's stator current and reducing its dimensions and noise, they do not allow for easier detection and parameter estimation when a defect appears, and sometimes help from an expert is needed to explain the signals. In this paper, the use of ANN-GA (Artificial Neural Networks-Genetic Algorithm) is investigated in combination with the best variant of the ESPRIT (Estimation of Signal Parameters via Rotational Invariant Techniques) method applied on stator current signals for efficient real-time bearing fault detection, to avoid any help being required from experts. MATLAB simulations of these variants revealed that the TLS variant highly discriminates the fault. By using this variant to prepare the data in the search of the best ANN model, in combination with genetic algorithms (used for optimizing the search of ANN parameters), two architectures capable of discriminating this bearing fault in real time in time and frequency domain were obtained.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.
- Creative Commons Copyright License
The journal allows readers to download and share all published articles as long as they properly cite such articles; however, they cannot change them or use them commercially. This is classified as CC BY-NC-ND for the creative commons license.
- Retention of Copyright and Publishing Rights
The journal allows the authors of the published articles to hold copyrights and publishing rights without restrictions.
References
S. Chakkor, M. Baghouri, and Abderrahmane Hajraoui, “High accuracy ESPRIT-TLS technique for wind turbine fault discrimination,” International Journal of Electrical and Computer Engineering, vol. 9, no. 1, pp. 122–131, 2015.
B. D. Ketelaere, M. Hubert, and E. Schmitt, “Overview of PCA-based statistical process-monitoring methods for time-dependent, high-dimensional data,” Journal of Quality Technology, vol. 47, no. 4, pp. 318–335, 2015.
Z. Husain, H. Malik, and M. A. Khan, “Recent trends in power transformer fault diagnosis and condition assessment,” Buletin Teknik Elektro dan Informatika, vol. 2, no. 2, pp. 95–104, Jun. 2013.
D. Wu et al., “An automatic bearing fault diagnosis method based on characteristics frequency ratio,” Sensors, vol. 20, no. 5, 2020, Art. no. 1519.
C. Abdelkrim, M. S. Meridjet, N. Boutasseta, and L. Boulanouar, “Detection and classification of bearing faults in industrial geared motors using temporal features and adaptive neuro-fuzzy inference system,” Heliyon, vol. 5, no. 8, Aug. 2019, Art. no. e02046.
B. Zhang, C. Sconyers, C. Byington, R. Patrick, M. E. Orchard, and G. Vachtsevanos, “A probabilistic fault detection approach: Application to bearing fault detection,” IEEE Transactions on Industrial Electronics, vol. 58, no. 5, pp. 2011–2018, May 2011.
J. Zarei, M. A. Tajeddini, and H. R. Karimi, “Vibration analysis for bearing fault detection and classification using an intelligent filter,” Mechatronics, vol. 24, no. 2, pp. 151–157, Mar. 2014.
P. Gupta and M. Pradhan, “Fault detection analysis in rolling element bearing: A review,” Materials Today: Proceedings, vol. 4, no. 2, pp. 2085–2094, 2017.
H.-Q. Wang, W. Hou, G. Tang, H.-F. Yuan, Q.-L. Zhao, and X. Cao, “Fault detection enhancement in rolling element bearings via peak-based multiscale decomposition and envelope demodulation,” Mathematical Problems in Engineering, vol. 2014, 2014, Art. no. 329458.
S. A. Chopade, J. A. Gaikwad, and J. V. Kulkarni, “Bearing fault detection using PCA and wavelet based envelope analysis,” in 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), Bangalore, India, 2016, pp. 248–253.
T. H. Mohamad and C. Nataraj, “Fault identification and severity analysis of rolling element bearings using phase space topology,” Journal of Vibration and Control, vol. 27, no. 3–4, pp. 295–310, 2021.
M. L. Masmoudi, E. Etien, S. Moreau, and A. Sakout, “Single point bearing fault diagnosis using simplified frequency model,” Electrical Engineering, vol. 99, pp. 455–465, Mar. 2017.
D. D. Susilo, A. Widodo, T. Prahasto, and M. Nizam, “Fault diagnosis of roller bearing using parameter evaluation technique and multi-class support vector machine,” AIP Conference Proceedings, vol. 1788, no. 1, 2017, Art. no. 030081.
Y. Imaouchen, R. Alkama, and M. Thomas, “Bearing fault detection using motor current signal analysis based on wavelet packet decomposition and Hilbert envelope,” MATEC Web of Conferences, vol. 20, 2015, Art. no. 03002.
S. Zhang, S. Zhang, B. Wang, and T. G. Habetler, “Deep learning algorithms for bearing fault diagnostics - a review,” in IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), Toulouse, France, 2019, pp. 257–263.
B. Aubert, “Détection des courts-circuits interspires dans les Générateurs Synchrones à Aimants Permanents : Méthodes basées modèles et filtre de Kalman étendu - Application à un canal de génération électrique en aéronautique,” Ph.D. dissertation, Institut National Polytechnique de Toulouse - INPT, Toulouse, France, 2014.
J. Chatelain, Machines électriques. Lausanne, Switzerland: Ecole Polytechnique Fédérale de Lausanne, 1983.
S. A. S. A. Kazzaz and G. Singh, “Experimental investigations on induction machine condition monitoring and fault diagnosis using digital signal processing techniques,” Electric Power Systems Research, vol. 65, no. 3, pp. 197–221, Jun. 2003.
W. T. Thomson and R. J. Gilmore, “Motor current signature analysis to detect faults in induction motor drives—fundamentals, data interpretation, and industrial case histories,” in Proceeding of the Thirty-Second Turbomachinery Symposium, 2003, pp. 145–156.
A. Gheitasi, “Motors fault recognition using distributed current signature analysis,” Ph.D. dissertation, Auckland University of Technology, New Zealand, 2013.
W. T. Thomson, “On-line motor current signature analysis prevents premature failure of large induction motor drives,” Maintenance and Asset Management, vol. 24, no. 3, pp. 30–35, May 2009.
S. Chakkor, “E-diagnostic de processus physiques à base des méthodes de haute résolution Application : machines éoliennes,” Ph.D. dissertation, Université Abdelmalek Essaâdi, Tangier, Morocco, 2015.
K. Kudelina et al., “Bearing fault analysis of BLDC motor for electric scooter application,” Designs, vol. 4, no. 4, 2020, Art. no. 42.
M. Unal, M. Onat, M. Demetgul, and H. Kucuk, “Fault diagnosis of rolling bearings using a genetic algorithm optimized neural network,” Measurement, vol. 58, pp. 187–196, Dec. 2014.
L. Jack and A. Nandi, “Genetic algorithms for feature selection in machine condition monitoring with vibration signals,” IEE Proceedings - Vision, Image, and Signal Processing, vol. 147, no. 3, pp. 205–212, Jun. 2000.