Co-design Model for Neuromorphic Technology Development in Rolling Element Bearing Condition Monitoring
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
This paper presents an end-to-end condition monitoring co-design model, from vibration measurement to anomaly detection, where conventional signal processing principles are combined with neuromorphic sensing and computing concepts to enable investigations of the potential improvements offered by brain-like information processing technologies.
The use of machine learning in condition monitoring became increasingly popular for intelligent fault diagnosis in the last decade, taking advantage of the rapid developments in deep learning.
However, the high computational cost of training and using deep neural networks prevents the use of such solutions for analysing the bulk of data generated by the resource constrained edge devices, i.e., the condition monitoring sensor systems, as only a minor fraction of data can be transmitted to the cloud or edge servers for analysis.
There is an untapped potential to process this data and thereby improve intelligent fault diagnosis models using event-triggered sensing, spiking neural networks, and neuromorphic processors that substantially can improve the energy efficiency and capacity of embedded machine learning condition monitoring solutions.
The proposed co-design model is evaluated on two use-cases involving rolling element bearing failures, one based on a labelled laboratory environment dataset, and one based on a wind turbine drivetrain bearing failure representing a real-world scenario with stochastic changes of machine state and unknown labels of the bearing condition.
By adjusting co-design parameters, the resulting hybrid conventional/neuromorphic model show a comparable accuracy in detection performance for the laboratory dataset compared to the state-of-the-art reported in the literature.
Similarly, for the wind turbine drivetrain dataset a bearing fault detection time comparable to that in previous work is obtained.
This shows the successful implementation of a hybrid conventional/neuromorphic co-design model for condition monitoring applications, offering novel opportunities to investigate performance trade-offs and efficiency improvements enabled by neuromorphic technologies.
How to Cite
##plugins.themes.bootstrap3.article.details##
neuromorphic computing, spiking neural networks
Aimone, J. B., Date, P., Fonseca-Guerra, G. A., Hamilton, K. E., Henke, K., Kay, B.,… Smith, J. D. (2022, ep). A review of non-cognitive applications for neuromorphic computing. Neuromorphic Computing and Engineering, 2(3), 032003. doi: 10.1088/2634-4386/ac889c
Bourdoukan, R., Barrett, D., Deneve, S., & Machens, C. K. (2012). Learning optimal spike-based representations. In F. Pereira, C. Burges, L. Bottou, & K. Weinberger (Eds.), Advances in neural information processing systems (Vol. 25). Curran Associates, Inc.
Cambou, P., & Tschudi, Y. (2019). Neuromorphic sensing and computing 2019 - market & technology report.
Dennler, N., Haessig, G., Cartiglia, M., & Indiveri, G. (2021). Online detection of vibration anomalies using balanced spiking neural networks. In 2021 IEEE 3rd international conference on artificial intelligence circuits and systems (aicas) (p. 1-4). doi: 10.1109/AICAS51828.2021.9458403
Dorigo, T., Giammanco, A., Vischia, P., Aehle, M., Bawaj, M., Boldyrev, A.,… Zaraket, H. (2023). Toward the end-to-end optimization of particle physics instruments with differentiable programming. Reviews in Physics, 10, 100085. doi: 10.1016/j.revip.2023.100085
Gerstner, W., Kistler, W. M., Naud, R., & Paninski, L. (2014). Neuronal dynamics: From single neurons to networks and models of cognition. Cambridge University Press.
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.
Martin-del-Campo, S., & Sandin, F. (2017). Online feature learning for condition monitoring of rotating machinery. Engineering Applications of Artificial Intelligence, 64, 187-196. doi: 10.1016/j.engappai.2017.06.012
Mehonic, A., & Kenyon, A. J. (2022). Brain-inspired computing needs a master plan. Nature, 604(7905), 255–260.
Nguyen, T., Jump, A., & Casey, D. (2023). Emerging tech impact radar: 2023.
Nilsson, M., Liwicki, F., & Sandin, F. (2022). Spatiotemporal pattern recognition in single mixed-signal VLSI neurons with heterogeneous dynamic synapses. In Proceedings of the international conference on neuromorphic systems 2022. New York, NY, USA: Association for Computing Machinery. doi: 10.1145/3546790.3546794
Qiu, S., Cui, X., Ping, Z., Shan, N., Li, Z., Bao, X., & Xu, X. (2023). Deep learning techniques in intelligent fault diagnosis and prognosis for industrial systems: A review. Sensors, 23(3). doi: 10.3390/s23031305
Saari, J., Strömbergsson, D., Lundberg, J., & Thomson, A. (2019). Detection and identification of windmill bearing faults using a one-class support vector machine. Measurement, 137, 287-301. doi: 10.1016/j.measurement.2019.01.020
Schuman, C. D., Kulkarni, S. R., Parsa, M., Mitchell, J. P., Date, P., & Kay, B. (2022). Opportunities for neuromorphic computing algorithms and applications. Nature Computational Science, 2(1), 10–19.
Strömbergsson, D., Marklund, P., Berglund, K., & Larsson, P.-E. (2021). Property requirements of vibration measurements in wind turbine drivetrain bearing condition monitoring. Insight - Non-Destructive Testing and Condition Monitoring, 63(11), 667-674. doi: 10.1784/insi.2021.63.11.667
Tavanaei, A., Ghodrati, M., Kheradpisheh, S. R., Masquelier, T., & Maida, A. (2019). Deep learning in spiking neural networks. Neural Networks, 111, 47-63. doi: 10.1016/j.neunet.2018.12.002
Wang, J., Li, T., Sun, C., Yan, R., & Chen, X. (2022). Improved spiking neural network for intershaft bearing fault diagnosis. Journal of Manufacturing Systems, 65, 208-219. doi: 10.1016/j.jmsy.2022.09.003
Ye, L., Wang, Z., Liu, Y., Chen, P., Li, H., Zhang, H.,… Huang, R. (2021). The challenges and emerging technologies for low-power artificial intelligence IoT systems. IEEE Transactions on Circuits and Systems I: Regular Papers, 68(12), 4821-4834. doi: 10.1109/TCSI.2021.3095622
Zuo, L., Xu, F., Zhang, C., Xiahou, T., & Liu, Y. (2022). A multi-layer spiking neural network-based approach to bearing fault diagnosis. Reliability Engineering & System Safety, 225, 108561. doi: 10.1016/j.ress.2022.108561
Zuo, L., Zhang, L., Zhang, Z.-H., Luo, X.-L., & Liu, Y. (2021). A spiking neural network based approach to bearing fault diagnosis. Journal of Manufacturing Systems, 61, 714-724. doi: 10.1016/j.jmsy.2020.07.003
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.