Implementation and Evaluation of Machine Learning Algorithms in Ball Bearing Fault Detection

[1] Achouch, M., Dimitrova, M., Ziane, K., Karganroudi, S. S., Dhouib, R., Ibrahim, H., Adda, M. (2022). On predictive maintenance in Industry 4.0: Overview, models, and challenges. Applied Sciences Review, 12 (16), 8081. https://doi.org/10.3390/app12168081 AchouchM. DimitrovaM. ZianeK. KarganroudiS. S. DhouibR. IbrahimH. AddaM. 2022 On predictive maintenance in Industry 4.0: Overview, models, and challenges Applied Sciences Review 12 16 8081 https://doi.org/10.3390/app12168081 Search in Google Scholar

[2] Lindh, T. (2003). On the condition monitoring of induction machines. Doctoral Dissertation, Lappeenranta University of Technology, Lappeenranta, Finland. https://urn.fi/URN:ISBN:951-764-843-X LindhT. 2003 On the condition monitoring of induction machines Doctoral Dissertation, Lappeenranta University of Technology, Lappeenranta, Finland. https://urn.fi/URN:ISBN:951-764-843-X Search in Google Scholar

[3] Tavner, P. J. (2008). Review of condition monitoring of rotating electrical machines. IET Electric Power Applications, 2 (4), 215–247. https://doi.org/10.1049/iet-epa:20070280 TavnerP. J. 2008 Review of condition monitoring of rotating electrical machines IET Electric Power Applications 2 4 215 247 https://doi.org/10.1049/iet-epa:20070280 Search in Google Scholar

[4] Sarma, N., Tuohy, P., Djurovic, S. (2023). Condition monitoring of rotating electrical machines. In Encyclopedia of Electrical and Electronic Power Engineering. Elsevier, 143–154. https://doi.org/10.1016/b978-0-12-821204-2.00136-7 SarmaN. TuohyP. DjurovicS. 2023 Condition monitoring of rotating electrical machines In Encyclopedia of Electrical and Electronic Power Engineering Elsevier 143 154 https://doi.org/10.1016/b978-0-12-821204-2.00136-7 Search in Google Scholar

[5] Motor Reliability Working Group. (1985). Report of large motor reliability survey of industrial and commercial installations, Part I. IEEE Transactions on Industry Applications, IA-21 (4), 853–864. https://doi.org/10.1109/TIA.1985.349532 Motor Reliability Working Group 1985 Report of large motor reliability survey of industrial and commercial installations, Part I IEEE Transactions on Industry Applications IA-21 (4), 853 864 https://doi.org/10.1109/TIA.1985.349532 Search in Google Scholar

[6] Motor Reliability Working Group. (1985). Report of large motor reliability survey of industrial and commercial installations, Part II. IEEE Transactions on Industry Applications, IA-21 (4), 865–872. https://doi.org/10.1109/TIA.1985.349533 Motor Reliability Working Group 1985 Report of large motor reliability survey of industrial and commercial installations, Part II IEEE Transactions on Industry Applications IA-21 (4), 865 872 https://doi.org/10.1109/TIA.1985.349533 Search in Google Scholar

[7] Albrecht, P. F., Appiarius, J. C., McCoy, R. M., Owen, E. L., Sharma, D. K. (1986). Assessment of the reliability of motors in utility applications – updated. IEEE Transactions on Energy Conversion, EC-1 (1), 39–46. https://doi.org/10.1109/TEC.1986.4765668 AlbrechtP. F. AppiariusJ. C. McCoyR. M. OwenE. L. SharmaD. K. 1986 Assessment of the reliability of motors in utility applications – updated IEEE Transactions on Energy Conversion EC-1 (1), 39 46 https://doi.org/10.1109/TEC.1986.4765668 Search in Google Scholar

[8] Thorsen, O. V., Dalva, M. (1995). A survey of faults on induction motors in offshore oil industry, petrochemical industry, gas terminals, and oil refineries. IEEE Transactions on Industry Applications, 31 (5), 1186–1196. https://doi.org/10.1109/28.464536 ThorsenO. V. DalvaM. 1995 A survey of faults on induction motors in offshore oil industry, petrochemical industry, gas terminals, and oil refineries IEEE Transactions on Industry Applications 31 5 1186 1196 https://doi.org/10.1109/28.464536 Search in Google Scholar

[9] Shin, K., Hammond, J. (2008). Fundamentals of Signal Processing for Sound and Vibration Engineers. Wiley, ISBN 978-0-470-51188-6. ShinK. HammondJ. 2008 Fundamentals of Signal Processing for Sound and Vibration Engineers Wiley ISBN 978-0-470-51188-6. Search in Google Scholar

[10] Trendafilova, I. (2010). An automated procedure for detection and identification of ball bearing damage using multivariate statistics and pattern recognition. Mechanical Systems and Signal Processing, 24 (6), 1858–1869. https://doi.org/10.1016/j.ymssp.2010.02.005 TrendafilovaI. 2010 An automated procedure for detection and identification of ball bearing damage using multivariate statistics and pattern recognition Mechanical Systems and Signal Processing 24 6 1858 1869 https://doi.org/10.1016/j.ymssp.2010.02.005 Search in Google Scholar

[11] Marić, D., Duspara, M., Šolić, T., Samardžić, I. (2019). Application of SVM models for classification of welded joints. Technical Gazette, 26 (2), 533–538. https://doi.org/10.17559/TV-20180305095253 MarićD. DusparaM. ŠolićT. SamardžićI. 2019 Application of SVM models for classification of welded joints Technical Gazette 26 2 533 538 https://doi.org/10.17559/TV-20180305095253 Search in Google Scholar

[12] Rozing, G., Duspara, M., Dudic, B., Savkovic, B. (2023). Research on the effect of load and rotation speed on resistance to combined wear of stainless steels using ANOVA analysis. Materials, 16 (12), 4284. https://doi.org/10.3390/ma16124284 RozingG. DusparaM. DudicB. SavkovicB. 2023 Research on the effect of load and rotation speed on resistance to combined wear of stainless steels using ANOVA analysis Materials 16 12 4284 https://doi.org/10.3390/ma16124284 Search in Google Scholar

[13] Pandian, A., Ali, A. (2010). A review of recent trends in machine diagnosis and prognosis algorithms. In 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC). IEEE. https://doi.org/10.1109/NABIC.2009.5393625 PandianA. AliA. 2010 A review of recent trends in machine diagnosis and prognosis algorithms In 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC) IEEE. https://doi.org/10.1109/NABIC.2009.5393625 Search in Google Scholar

[14] Liu, R., Yang, B., Zio, E., Chen, X. (2018). Artificial intelligence for fault diagnosis of rotating machinery: A review. Mechanical Systems and Signal Processing, 108, 33–47. https://doi.org/10.1016/j.ymssp.2018.02.016 LiuR. YangB. ZioE. ChenX. 2018 Artificial intelligence for fault diagnosis of rotating machinery: A review Mechanical Systems and Signal Processing 108 33 47 https://doi.org/10.1016/j.ymssp.2018.02.016 Search in Google Scholar

[15] Liu, X., Zhou, Q., Zhao, J., Shen, H., Xiong, X. (2019). Fault diagnosis of rotating machinery under noisy environment conditions based on a 1-D convolutional autoencoder and 1-D convolutional neural network. Sensors, 19 (4), 972. https://doi.org/10.3390/s19040972 LiuX. ZhouQ. ZhaoJ. ShenH. XiongX. 2019 Fault diagnosis of rotating machinery under noisy environment conditions based on a 1-D convolutional autoencoder and 1-D convolutional neural network Sensors 19 4 972 https://doi.org/10.3390/s19040972 Search in Google Scholar

[16] Samanta, B., Al-Balushi, K. R. (2003). Artificial neural network based fault diagnostics of rolling element bearings using time-domain features. Mechanical Systems and Signal Processing, 17 (2), 317–328. https://doi.org/10.1006/mssp.2001.1462 SamantaB. Al-BalushiK. R. 2003 Artificial neural network based fault diagnostics of rolling element bearings using time-domain features Mechanical Systems and Signal Processing 17 2 317 328 https://doi.org/10.1006/mssp.2001.1462 Search in Google Scholar

[17] Kankar, P. K., Sharma, S. C., Harsha, S. P. (2011). Fault diagnosis of ball bearings using machine learning methods. Expert Systems with Applications, 38 (3), 1876–1886. https://doi.org/10.1016/j.eswa.2010.07.119 KankarP. K. SharmaS. C. HarshaS. P. 2011 Fault diagnosis of ball bearings using machine learning methods Expert Systems with Applications 38 3 1876 1886 https://doi.org/10.1016/j.eswa.2010.07.119 Search in Google Scholar

[18] Jenkins, C. D. (2019). Bearing fault detection and wear estimation using machine learning. Technical Report LA-UR-19-27700. https://doi.org/10.2172/1557163 JenkinsC. D. 2019 Bearing fault detection and wear estimation using machine learning Technical Report LA-UR-19-27700. https://doi.org/10.2172/1557163 Search in Google Scholar

[19] Sawaqed, L. S., Alrayes, A. M. (2020). Bearing fault diagnostic using machine learning algorithms. Progress in Artificial Intelligence, 9 (4), 341–350. https://doi.org/10.1007/s13748-020-00217-z SawaqedL. S. AlrayesA. M. 2020 Bearing fault diagnostic using machine learning algorithms Progress in Artificial Intelligence 9 4 341 350 https://doi.org/10.1007/s13748-020-00217-z Search in Google Scholar

[20] Zhang, S., Zhang, S., Wang, B., Habetler, T. G. (2020). Deep learning algorithms for bearing fault diagnostics—A comprehensive review. IEEE Access, 8, 29857–29881. https://doi.org/10.1109/ACCESS.2020.2972859 ZhangS. ZhangS. WangB. HabetlerT. G. 2020 Deep learning algorithms for bearing fault diagnostics—A comprehensive review IEEE Access 8 29857 29881 https://doi.org/10.1109/ACCESS.2020.2972859 Search in Google Scholar

[21] Teotrakool, K., Devaney, M. J., Eren, L. (2008). Bearing fault detection in adjustable speed drives via a support vector machine with feature selection using a genetic algorithm. In 2008 IEEE Instrumentation and Measurement Technology Conference. IEEE, 1129–1133. https://doi.org/10.1109/IMTC.2008.4547208 TeotrakoolK. DevaneyM. J. ErenL. 2008 Bearing fault detection in adjustable speed drives via a support vector machine with feature selection using a genetic algorithm In 2008 IEEE Instrumentation and Measurement Technology Conference IEEE, 1129 1133 https://doi.org/10.1109/IMTC.2008.4547208 Search in Google Scholar

[22] Konar, P., Chattopadhyay, P. (2011). Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs). Applied Soft Computing, 11 (6), 4203–4211. https://doi.org/10.1016/j.asoc.2011.03.014 KonarP. ChattopadhyayP. 2011 Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs) Applied Soft Computing 11 6 4203 4211 https://doi.org/10.1016/j.asoc.2011.03.014 Search in Google Scholar

[23] Benkedjouh, T., Medjaher, K., Zerhouni, N., Rechak, S. (2012). Fault prognostic of bearings by using support vector data description. In 2012 IEEE Conference on Prognostics and Health Management. IEEE, 1–7. https://doi.org/10.1109/ICPHM.2012.6299511 BenkedjouhT. MedjaherK. ZerhouniN. RechakS. 2012 Fault prognostic of bearings by using support vector data description In 2012 IEEE Conference on Prognostics and Health Management IEEE, 1 7 https://doi.org/10.1109/ICPHM.2012.6299511 Search in Google Scholar

[24] Kang, M., Kim, J., Kim, J. M., Tan, A. C., Kim, E. Y., Choi, B. K. (2015). Reliable fault diagnosis for low-speed bearings using individually trained support vector machines with kernel discriminative feature analysis. IEEE Transactions on Power Electronics, 30 (5), 2786–2797. https://doi.org/10.1109/TPEL.2014.2358494 KangM. KimJ. KimJ. M. TanA. C. KimE. Y. ChoiB. K. 2015 Reliable fault diagnosis for low-speed bearings using individually trained support vector machines with kernel discriminative feature analysis IEEE Transactions on Power Electronics 30 5 2786 2797 https://doi.org/10.1109/TPEL.2014.2358494 Search in Google Scholar

[25] Song, W., Xiang, J. (2017). A method using numerical simulation and support vector machine to detect faults in bearings. In 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC). IEEE, 603–607. https://doi.org/10.1109/SDPC.2017.118 SongW. XiangJ. 2017 A method using numerical simulation and support vector machine to detect faults in bearings In 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC) IEEE, 603 607 https://doi.org/10.1109/SDPC.2017.118 Search in Google Scholar

[26] Pandarakone, S. E., Mizuno, Y., Nakamura, H. (2019). Evaluating the progression and orientation of scratches on outer-raceway bearing using a pattern recognition method. IEEE Transactions on Industrial Electronics, 66 (2), 1307–1314. https://doi.org/10.1109/TIE.2018.2833025 PandarakoneS. E. MizunoY. NakamuraH. 2019 Evaluating the progression and orientation of scratches on outer-raceway bearing using a pattern recognition method IEEE Transactions on Industrial Electronics 66 2 1307 1314 https://doi.org/10.1109/TIE.2018.2833025 Search in Google Scholar

[27] King, R. D., Feng, C., Sutherland, A. (1995). StatLog: Comparison of classification algorithms on large real-world problems. Applied Artificial Intelligence, 9 (3), 289–333. https://doi.org/10.1080/08839519508945477 KingR. D. FengC. SutherlandA. 1995 StatLog: Comparison of classification algorithms on large real-world problems Applied Artificial Intelligence 9 3 289 333 https://doi.org/10.1080/08839519508945477 Search in Google Scholar

[28] Kohavi, R., John, G. H. (1995). Automatic parameter selection by minimizing estimated error. In Machine Learning Proceedings 1995. Morgan Kaufmann Publishers, 304–312. https://doi.org/10.1016/B978-1-55860-377-6.50045-1 KohaviR. JohnG. H. 1995 Automatic parameter selection by minimizing estimated error In Machine Learning Proceedings 1995 Morgan Kaufmann Publishers 304 312 https://doi.org/10.1016/B978-1-55860-377-6.50045-1 Search in Google Scholar

[29] Michie, D., Spiegelhalter, D. J., Taylor, C. C. (1994). Machine Learning, Neural and Statistical Classification. Prentice Hall, ISBN 978-0131063600. MichieD. SpiegelhalterD. J. TaylorC. C. 1994 Machine Learning, Neural and Statistical Classification Prentice Hall ISBN 978-0131063600. Search in Google Scholar

[30] Ripley, B. D. (1993). Statistical aspects of neural networks. In Networks and Chaos - Statistical and Probabilistic Aspects. Champman & Hall, 40–123. ISBN 0-412-46530-2. RipleyB. D. 1993 Statistical aspects of neural networks In Networks and Chaos - Statistical and Probabilistic Aspects Champman & Hall 40 123 ISBN 0-412-46530-2. Search in Google Scholar

[31] Hutter, F., Kotthoff, L., Vanschoren, J. (eds.) (2019). Automated Machine Learning: Methods, Systems, Challenges. Springer, ISBN 978-3-030-05318-5. https://doi.org/10.1007/978-3-030-05318-5 HutterF. KotthoffL. VanschorenJ. (eds.) 2019 Automated Machine Learning: Methods, Systems, Challenges Springer ISBN 978-3-030-05318-5. https://doi.org/10.1007/978-3-030-05318-5 Search in Google Scholar

[32] Shahriari, B., Swersky, K., Wang, Z., Adams, R., de Freitas, N. (2016). Taking the human out of the loop: A review of Bayesian optimization. Proceedings of the IEEE, 104 (1), 148–175. https://doi.org/10.1109/JPROC.2015.2494218 ShahriariB. SwerskyK. WangZ. AdamsR. de FreitasN. 2016 Taking the human out of the loop: A review of Bayesian optimization Proceedings of the IEEE 104 1 148 175 https://doi.org/10.1109/JPROC.2015.2494218 Search in Google Scholar

[33] Brochu, E., Cora, V. M., De Freitas, N. (2010). A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. arXiv, 1012.2599. https://doi.org/10.48550/arXiv.1012.2599 BrochuE. CoraV. M. De FreitasN. 2010 A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning arXiv, 1012.2599. https://doi.org/10.48550/arXiv.1012.2599 Search in Google Scholar