Fault Severity Estimation in Cracked Shafts by Integration of Phase Space Topology and Convolutional Neural Network

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Oct 26, 2023
Utkarsh Andharikar Amirhassan Abbasi Prashant Kambali C. Nataraj

Abstract

With the rapid advancement of industrial systems and the unavoidable complications and interconnectedness in systems, diagnostics of industrial machinery are achieving paramount importance. Accurate estimation of health condition of industrial machinery becomes more challenging due to the inherent nonlinearity, complexity, and uncertainty of the observations. Nonlinear dynamic analysis has proven to be a powerful tool for providing information about the health condition of a system that can be used for diagnostic applications. The current study particularly focuses on crack depth estimation using phase space analysis. Phase space provides a topological representation of the dynamics of the system and is highly informative about the health condition. The information suitable for diagnostics is employed by Convolutional Neural Networks, which are known to be powerful in extracting spatial information from maps. The proposed diagnostic method is evaluated on a Jeffcott rotor model with transverse crack in the rotating shaft to estimate the severity of the fault from the phase space topology as a case study.

How to Cite

Andharikar, . U., Abbasi, A., Kambali, P., & Nataraj, C. (2023). Fault Severity Estimation in Cracked Shafts by Integration of Phase Space Topology and Convolutional Neural Network. Annual Conference of the PHM Society, 15(1). https://doi.org/10.36001/phmconf.2023.v15i1.3574
Abstract 179 | PDF Downloads 128

##plugins.themes.bootstrap3.article.details##

Keywords

Convolutional neural network, Rotordynamics, Phase space topology

References
Abbasi, A., Nazari, F., & Nataraj, C. (2020). On modeling of vibration and crack growth in a rotor for prognostics. In In proceedings of the annual conference of the phm society 2020 (Vol. 12). Retrieved from https://doi.org/10.36001/ phmconf.2020.v12i1.1193

Abbasi, A., Nazari, F., & Nataraj, C. (2022, dec). Adaptive modeling of vibrations and structural fatigue for analyzing crack propagation in a rotating system. Journal of Sound and Vibration, 541, 117276. doi: 10.1016/ j.jsv.2022.117276

Abdel-Hamid, O., Mohamed, A.-r., Jiang, H., & Penn, G. (2012). Applying convolutional neural networks concepts to hybrid nn-hmm model for speech recognition. In 2012 ieee international conference on acoustics, speech and signal processing (icassp) (p. 4277-4280). doi: 10.1109/ICASSP.2012.6288864

Acharya, U. R., Oh, S. L., Hagiwara, Y., Tan, J. H., & Adeli, H. (2018, sep). Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Computers in Biology and Medicine, 100, 270–278. doi: 10.1016/j.compbiomed.2017.09.017

Alzarooni, T., Al-Shudeifat, M. A., Shiryayev, O., & Nataraj, C. (2020, oct). Breathing crack model effect on rotor's postresonance backward whirl. Journal of Computational and Nonlinear Dynamics, 15(12). doi: https:// doi.org/10.1115/1.4048358

Caponetto, R., Rizzo, F., Russotti, L., & Xibilia, M. G. (2019). Deep learning algorithm for predictive maintenance of rotating machines through the analysis of the orbits shape of the rotor shaft. In Proceedings of the 1st international conference on smart innovation, ergonomics and applied human factors (SEAHF) (pp. 245–250). Springer International Publishing. doi: 10.1007/978-3-030-22964-1 25

Carroll, T. L. (2015, jan). Attractor comparisons based on density. Chaos: An Interdisciplinary Journal of Nonlinear Science, 25(1), 013111. doi: 10.1063/1 .4906342

Chandra, N. H., & Sekhar, A. (2016, may). Fault detection in rotor bearing systems using time frequency techniques. Mechanical Systems and Signal Processing, 72-73, 105–133. doi: https://doi.org/10.1016/j.ymssp .2015.11.013

Debayle, J., Hatami, N., & Gavet, Y. (2018, apr). Classification of time-series images using deep convolutional neural networks. doi: 10.1117/12.2309486

Gasch, R. (1993, jan). A survey of the dynamic behaviour of a simple rotating shaft with a transverse crack. Journal of Sound and Vibration, 160(2), 313–332. doi: 10.1006/ jsvi.1993.1026

Gasch, R. (2008). Dynamic behaviour of the laval rotor with a transverse crack. Mechanical Systems and Signal Processing, 22(4), 790-804. Retrieved from https://doi.org/10.1016/j .ymssp.2007.11.023 (Special Issue: Crack Effects in Rotordynamics)

Gomez, M., Castejon, C., & Garcia-Prada, J. (2016, feb). Crack detection in rotating shafts based on 3 × energy: Analytical and experimental analyses. Mechanism and Machine Theory, 96, 94–106. doi: https://doi.org/10 .1016/j.mechmachtheory.2015.09.009

Guo, C., AL-Shudeifat, M., Yan, J., Bergman, L., McFarland, D., & Butcher, E. (2013, aug). Application of empirical mode decomposition to a jeffcott rotor with a breathing crack. Journal of Sound and Vibration, 332(16), 3881– 3892. doi: https://doi.org/10.1016/j.jsv.2013.02.031

Guo, D., & Peng, Z. (2007, nov). Vibration analysis of a cracked rotor using hilbert–huang transform. Mechanical Systems and Signal Processing, 21(8), 3030–3041. doi: https://doi.org/10.1016/j.ymssp.2007.05.004

Imam, I., Azzaro, S. H., Bankert, R. J., & Scheibel, J. (1989, jul). Development of an on-line rotor crack detection and monitoring system. Journal of Vibration and Acoustics, 111(3), 241–250. doi: 10.1115/1.3269848

Ince, T., Kiranyaz, S., Eren, L., Askar, M., & Gabbouj, M. (2016, nov). Real-time motor fault detection by 1- d convolutional neural networks. IEEE Transactions on Industrial Electronics, 63(11), 7067–7075. doi: 10.1109/TIE.2016.2582729

Jiang, X., Wang, F., Zhao, H., Xu, S., & Lin, L. (2020, nov). Novel orbit-based CNN model for automatic fault identification of rotating machines. Annual Conference of the PHM Society, 12(1), 7. doi: 10.36001/ phmconf.2020.v12i1.1147

Khan, A., Ko, D.-K., Lim, S. C., & Kim, H. S. (2019, mar). Structural vibration-based classification and prediction of delamination in smart composite laminates using deep learning neural network. Composites Part B: Engineering, 161, 586–594. doi: 10.1016/j.compositesb .2018.12.118

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In F. Pereira, C. Burges, L. Bottou, & K. Weinberger (Eds.), Advances in neural information processing systems (Vol. 25). Curran Associates, Inc. Retrieved from https://proceedings.neurips .cc/paper files/paper/2012/file/ c399862d3b9d6b76c8436e924a68c45b -Paper.pdf

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017, may). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84–90. doi: 10.1145/3065386

Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324. doi: 10.1109/5.726791

Li, B., Zhang, C., & He, Z. (2012, sep). HHT-based crack identification method for start-up rotor. Frontiers of Mechanical Engineering, 7(3), 300–304. doi: https:// doi.org/10.1007/s11465-012-0328-1

Lin, L., & Chu, F. (2012, jan). HHT-based AE characteristics of natural fatigue cracks in rotating shafts. Mechanical Systems and Signal Processing, 26, 181–189. doi: https://doi.org/10.1016/j.ymssp.2011.07.017

Lin, M., Chen, Q., & Yan, S. (2013). Network in network. doi: 10.48550/arXiv.1312.4400

Mohamad, T. H., Abbasi, A., Kim, E., & Nataraj, C. (2021, jun). Application of deep CNN-LSTM network to gear fault diagnostics. doi: 10.1109/ICPHM51084.2021.9486591

Mohamad, T. H., & Nataraj, C. (2020, may). Fault identification and severity analysis of rolling element bearings using phase space topology. Journal of Vibration and Control, 27(3-4), 295–310. doi: 10.1177/ 1077546320926293

Mohamad, T. H., Nazari, F., & Nataraj, C. (2019, jun). A review of phase space topology methods for vibrationbased fault diagnostics in nonlinear systems. Journal of Vibration Engineering & ; Technologies, 8(3), 393–401. doi: 10.1007/s42417-019-00157-6

Nelson, H. D., & Nataraj, C. (1986, apr). The dynamics of a rotor system with a cracked shaft. Journal of Vibration and Acoustics, 108(2), 189–196. doi: https://doi.org/ 10.1115/1.3269321

Patel, T. H., & Darpe, A. K. (2008). Influence of crack breathing model on nonlinear dynamics of a cracked rotor. Journal of Sound and Vibration, 311(3), 953-972. doi: https://doi.org/10.1016/j.jsv.2007.09.033

Raghu, S., Sriraam, N., Temel, Y., Rao, S. V., & Kubben, P. L. (2020, apr). EEG based multi-class seizure type classification using convolutional neural network and transfer learning. Neural Networks, 124, 202–212. doi: 10.1016/j.neunet.2020.01.017

Samadani, M., Kwuimy, C. A. K., & Nataraj, C. (2016, aug). Characterization of the nonlinear response of defective multi-DOF oscillators using the method of phase space topology (PST). Nonlinear Dynamics, 86(3), 2023– 2034. doi: 10.1007/s11071-016-3012-x

Sekhar, A. S. (2005, sep). Identification of unbalance and crack acting simultaneously in a rotor system: Modal expansion versus reduced basis dynamic expansion. Journal of Vibration and Control, 11(9), 1125–1145. doi: https://doi.org/10.1177/1077546305042531

Shao, S., McAleer, S., Yan, R., & Baldi, P. (2019, apr). Highly accurate machine fault diagnosis using deep transfer learning. IEEE Transactions on Industrial Informatics, 15(4), 2446–2455. doi: 10.1109/TII.2018 .2864759

Shudeifat, M. A. A., & Nataraj, C. (2020). Post-resonance backward whirl in a jeffcott rotor with a breathing crack model., 485–491. doi: https://doi.org/10.1007/978-3 -030-34713-0 48

Wang, Z., & Oates, T. (2015). Imaging time-series to improve classification and imputation. doi: 10.48550/arXiv.1506.00327

Wang, Z., Oates, T., et al. (2015). Encoding time series as images for visual inspection and classification using tiled convolutional neural networks.

Wang, Z., & Yang, J. (2017). Diabetic retinopathy detection via deep convolutional networks for discriminative localization and visual explanation. doi: 10.48550/arXiv.1703.10757

Wu, B., Feng, S., Sun, G., Xu, L., & Ai, C. (2019, dec). Fine-grained fault recognition method for shaft orbit of rotary machine based on convolutional neural network. Journal of Vibroengineering, 21(8), 2106–2120. doi: 10.21595/jve.2019.20359

Zheng, Y., Liu, Q., Chen, E., Ge, Y., & Zhao, J. L. (2014). Time series classification using multi-channels deep convolutional neural networks., 298–310. doi: 10 .1007/978-3-319-08010-9 33

Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016, jun). Learning deep features for discriminative localization. doi: 10.1109/CVPR.2016.319

Zhou, X., Jin, K., Shang, Y., & Guo, G. (2020, jul). Visually interpretable representation learning for depression recognition from facial images. IEEE Transactions on Affective Computing, 11(3), 542–552. doi: 10.1109/TAFFC.2018.2828819
Section
Technical Research Papers

Most read articles by the same author(s)