Precision fault prediction in motor bearings with feature selection and deep learning

Deep Prakash Singh; Sandip Kumar Singh

doi:10.52756/ijerr.2023.v32.035

Deep Prakash Singh Department of Mechanical Engineering, VBS Purvanchal University, Jaunpur, UP, India https://orcid.org/0009-0008-8931-0393
Sandip Kumar Singh Department of Mechanical Engineering, VBS Purvanchal University, Jaunpur, UP, India

DOI: https://doi.org/10.52756/ijerr.2023.v32.035

Keywords: Convolutional Neural Network, Feed Forward Neural Network and Radial Based Networ, Correlation and Chi-Square

Abstract

In the disciplines of industrial machinery, mechanical engineering is beneficial to recognize motor performance for motors with HP power, torque transducer, dynamometer, and control electronics. The motivation is to address the need for more accurate and efficient fault prediction in machinery to prevent breakdowns, reduce maintenance costs, and improve overall reliability. In this work, deep learning classifiers used to classify ball defect inner race fault, outer race fault and normal motor performance in testing. With the aid of three distinct classifiers CNN, FFNN, and RBN; these suggested relative characteristics are assessed. In comparison to other current algorithms, the suggested methodology for classifying motor performance achieved maximum accuracy in each CNN test at 95.4% and 97.7%. The correlation and chi-square algorithms are used to find out the added characteristics and rank of features. The correlation technique provides relations between attributes, and the chi-square offers the optimal balance between precision and feature space. We discovered that the performance is enhanced overall by relative power characteristics. The suggested models might offer rapid responses with less complexity.

References

Abdelmoumin, G., Rawat, D. B., & Rahman, A. (2021). On the performance of machine learning models for anomaly-based intelligent intrusion detection systems for the internet of things. IEEE Internet of Things Journal, 9(6), 4280-4290. https://doi.org/10.1109/JIOT.2021.3103829.

Ahmed, N., Ngadi, A. B., Sharif, J. M., Hussain, S., Uddin, M., Rathore, M. S., ... & Zuhra, F. T. (2022). Network threat detection using machine/deep learning in sdn-based platforms: a comprehensive analysis of state-of-the-art solutions, discussion, challenges, and future research direction. Sensors, 22(20), 7896. https://doi.org/10.3390/s22207896 PMID: 36298244

Alharbi, A., Alosaimi, W., Alyami, H., Rauf, H. T., & Damaševičius, R. (2021). Botnet attack detection using local global best bat algorithm for industrial internet of things. Electronics, 10(11), 1341. https://doi.org/10.3390/electronics10111341.

Ali, M. H., Jaber, M. M., Abd, S. K., Rehman, A., Awan, M. J., Damaševičius, R., & Bahaj, S. A. (2022). Threat analysis and distributed denial of service (DDoS) attack recognition in the internet of things (IoT). Electronics, 11(3), 494. https://doi.org/10.3390/electronics11030494.

Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., ... & Farhan, L. (2021). Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. Journal of big Data, 8, 1-74. https://doi.org/10.1186/s40537-021-00444-8.

Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M. S., ... & Asari, V. K. (2018). The history began from alexnet: A comprehensive survey on deep learning approaches. arXiv preprint arXiv:1803.01164. https://doi.org/10.48550/arXiv.1803.01164.

Amirabadi, M. A., Kahaei, M. H., & Nezamalhosseini, S. A. (2020). Novel suboptimal approaches for hyperparameter tuning of deep neural network [under the shelf of optical communication]. Physical Communication, 41, 101057. https://doi.org/10.1016/j.phycom.2020.101057.

Anderson, W. S., & Lenz, F. A. (2011). Review of motor and phantom related imagery. Neuroreport, 22(17), 939.

Banerjee, M., Goyal, R., Gupta, P., & Tripathi, A. (2023). Real-Time Face Recognition System with Enhanced Security Features using Deep Learning. International Journal of Experimental Research and Review, 32, 131-144. Retrieved from https://qtanalytics.in/journals/index.php/IJERR/article/view/2488

Bonassi, G., Biggio, M., Bisio, A., Ruggeri, P., Bove, M., & Avanzino, L. (2017). Provision of somatosensory inputs during motor imagery enhances learning-induced plasticity in human motor cortex. Scientific reports, 7(1), 9300.

Caroline Müllenbroich, M., McGhee, E. J., Wright, A. J., Anderson, K. I., & Mathieson, K. (2014). Strategies to overcome photobleaching in algorithm-based adaptive optics for nonlinear in-vivo imaging. Journal of Biomedical Optics, 19(1), 016021-016021. https://doi.org/10.1117/1.JBO.19.1.016021.

Cooney, C., Folli, R., & Coyle, D. (2019, October). Optimizing layers improves CNN generalization and transfer learning for imagined speech decoding from EEG. In 2019 IEEE international conference on systems, man and cybernetics (SMC) (pp. 1311-1316). IEEE. https://doi.org/10.1109/SMC.2019.8914246.

Coyle, D., Stow, J., McCreadie, K., McElligott, J., & Carroll, Á. (2015). Sensorimotor modulation assessment and brain-computer interface training in disorders of consciousness. Archives of physical medicine and rehabilitation, 96(3), S62-S70.

Duan, X., Xie, S., Xie, X., Meng, Y., & Xu, Z. (2019). Quadcopter flight control using a non-invasive multi-modal brain computer interface. Frontiers in neurorobotics, 13, 23.

Elmasry, W., Akbulut, A., & Zaim, A. H. (2020). Evolving deep learning architectures for network intrusion detection using a double PSO metaheuristic. Computer Networks, 168, 107042. https://doi.org/10.1016/j.comnet.2019.107042

Ferrag, M. A., Shu, L., Djallel, H., & Choo, K. K. R. (2021). Deep learning-based intrusion detection for distributed denial of service attack in agriculture 4.0. Electronics, 10(11), 1257. https://doi.org/10.3390/electronics10111257.

Gabrié, M., Manoel, A., Luneau, C., Macris, N., Krzakala, F., & Zdeborová, L. (2018). Entropy and mutual information in models of deep neural networks. Advances in Neural Information Processing Systems, 31.

Hussein, M., & Özyurt, F. (2021). A new technique for sentiment analysis system based on deep learning using Chi-Square feature selection methods. Balkan Journal of Electrical and Computer Engineering, 9(4), 320-326.

Lee, W. Y., Park, S. M., & Sim, K. B. (2018). Optimal hyperparameter tuning of convolutional neural networks based on the parameter-setting-free harmony search algorithm. Optik, 172, 359-367. https://doi.org/10.1016/j.ijleo.2018.07.044.

Lokku, G., Reddy, G. H., & Prasad, M. G. (2022). OPFaceNet: OPtimized Face Recognition Network for noise and occlusion affected face images using Hyperparameters tuned Convolutional Neural Network. Applied Soft Computing, 117, 108365. https://doi.org/10.1016/j.asoc.2021.108365.

Munzert, J., Lorey, B., & Zentgraf, K. (2009). Cognitive motor processes: the role of motor imagery in the study of motor representations. Brain research reviews, 60(2), 306-326.

Parra, G. D. L. T., Rad, P., Choo, K. K. R., & Beebe, N. (2020). Detecting Internet of Things attacks using distributed deep learning. Journal of Network and Computer Applications, 163, 102662. https://doi.org/10.1016/j.jnca.2020.102662.

Phothisonothai, M., & Nakagawa, M. (2008). EEG-based classification of motor imagery tasks using fractal dimension and neural network for brain-computer interface. IEICE TRANSACTIONS on Information and Systems, 91(1), 44-53.

Rao, M., Kumar, S., & Rao, K. (2023). Effective medical leaf identification using hybridization of GMM-CNN. International Journal of Experimental Research and Review, 32, 115-123. Retrieved from https://qtanalytics.in/journals/index.php/IJERR/article/view/2486

Reddy, N. S., & Khanaa, V. (2023). Diagnosing and categorizing of pulmonary diseases using Deep learning conventional Neural network. International Journal of Experimental Research and Review, 31(Spl Volume), 12-22. https://doi.org/10.52756/10.52756/ijerr.2023.v31spl.002

Sakr, M. M., Tawfeeq, M. A., & El-Sisi, A. B. (2019). Network intrusion detection system based PSO-SVM for cloud computing. International Journal of Computer Network and Information Security, 11(3), 22. https://doi.org/10.5815/ijcnis.2019.03.04

Schlögl, A., Vidaurre, C., & Müller, K. R. (2010). Adaptive methods in BCI research-an introductory tutorial. Berlin, Heidelberg: Springer Berlin Heidelberg. In Brain-Computer Interfaces: Revolutionizing Human-Computer Interaction, pp. 331-355.

Song, X., Shibasaki, R., Yuan, N. J., Xie, X., Li, T., & Adachi, R. (2017). DeepMob: learning deep knowledge of human emergency behavior and mobility from big and heterogeneous data. ACM Transactions on Information Systems (TOIS), 35(4), 1-19.

Song, X., Yoon, S. C., & Perera, V. (2013, November). Adaptive common spatial pattern for single-trial EEG classification in multisubject BCI. In 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER) (pp. 411-414).

Thapa, N., Liu, Z., Shaver, A., Esterline, A., Gokaraju, B., & Roy, K. (2021). Secure cyber defense: An analysis of network intrusion-based dataset CCD-idsv1 with machine learning and deep learning models. Electronics, 10(15), 1747. https://doi.org/10.3390/electronics10151747.

Tridawati, A., Wikantika, K., Susantoro, T. M., Harto, A. B., Darmawan, S., Yayusman, L. F., & Ghazali, M. F. (2020). Mapping the distribution of coffee plantations from multi-resolution, multi-temporal, and multi-sensor data using a random forest algorithm. Remote Sensing, 12(23), 3933.

Vartouni, A. M., Kashi, S. S., & Teshnehlab, M. (2018, February). An anomaly detection method to detect web attacks using stacked auto-encoder. In 2018 6th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS), pp. 131-134. https://doi.org/10.1109/CFIS.2018.8336654.

Vidaurre, C., Schlogl, A., Cabeza, R., Scherer, R., & Pfurtscheller, G. (2007). Study of on-line adaptive discriminant analysis for EEG-based brain computer interfaces. IEEE transactions on biomedical engineering, 54(3), 550-556.

Woehrle, H., Krell, M. M., Straube, S., Kim, S. K., Kirchner, E. A., & Kirchner, F. (2015). An adaptive spatial filter for user-independent single trial detection of event-related potentials. IEEE Transactions on Biomedical Engineering, 62(7), 1696-1705.

Wu, J., Chen, X. Y., Zhang, H., Xiong, L. D., Lei, H., & Deng, S. H. (2019). Hyperparameter optimization for machine learning models based on Bayesian optimization. Journal of Electronic Science and Technology, 17(1), 26-40. https://doi.org/10.11989/JEST.1674-862X.80904120.

Yuan, H., & He, B. (2014). Brain–computer interfaces using sensorimotor rhythms: current state and future perspectives. IEEE Transactions on Biomedical Engineering, 61(5), 1425-1435.

Yuan, X., Ou, C., Wang, Y., Yang, C., & Gui, W. (2020). Deep quality-related feature extraction for soft sensing modeling: A deep learning approach with hybrid VW-SAE. Neurocomputing, 396, 375-382. https://doi.org/10.1016/j.neucom.2018.11.107.

Zhong, Z., Jin, L., & Xie, Z. (2015, August). High performance offline handwritten chinese character recognition using googlenet and directional feature maps. In 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 846-850. https://doi.org/10.1109/ICDAR.2015.7333881.