Abstract
Electric machines and motors have been the subject of enormous development. New concepts in design and control allow expanding their applications in different fields. The vast amount of data have been collected almost in any domain of interest. They can be static; that is to say, they represent real-world processes at a fixed point of time. Vibration analysis and vibration monitoring, including how to detect and monitor anomalies in vibration data are widely used techniques for predictive maintenance in high-speed rotating machines. However, accurately identifying the presence of a bearing fault can be challenging in practice, especially when the failure is still at its incipient stage, and the signal-to-noise ratio of the monitored signal is small. The main objective of this work is to design a system that will analyze the vibration signals of a rotating machine, based on recorded data from sensors, in the time/frequency domain. As a consequence of such substantial interest, there has been a dramatic increase of interest in applying Machine Learning (ML) algorithms to this task. An ML system will be used to classify and detect abnormal behavior and recognize the different levels of machine operation modes (normal, degraded, and faulty). The proposed solution can be deployed as predictive maintenance for Industry 4.0.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Nembhard, A.D., Sinha, J.K., Elbhbah, K., Pinkerton, A.J.: Fault diagnosis of rotating machines using vibration and bearing temperature measurements. Diagnostyka 14(3), 45–52 (2013)
Tabaszewski, M.: Optimization of a nearest neighbours classifier for diagnosis of condition of rolling bearings. Diagnostyka 15(1), 37–42 (2014)
Astolfi, D., Castellani, F., Terzi, L.: Fault prevention and diagnosis through SCADA temperature data analysis of an onshore wind farm. Diagnostyka 15, 71–78 (2014)
Chehri, A., Jeon, G.: The Industrial Internet of Things: Examining How the IIoT will Improve the Predictive Maintenance. Lecture Notes of the Institute for Computer Sciences, Smart Innovation Systems and Technologies. Springer, Heidelberg (2019)
Chehri, A., Jeon, G.: Routing Protocol in the Industrial Internet of Things for Smart Factory Monitoring. Lecture Notes of the Institute for Computer Sciences, Smart Innovation Systems and Technologies. Springer, Heidelberg (2019)
Jeon, G., Awais, A., Chehri, A., Cuomo, S.: Special Issue on Video and Imaging Systems for Critical Engineering Applications. Multimedia Tools and Applications, Springer (2020)
Jeon, G., Chehri, A, Cuomo, S, Din. S, Jabbar, S.: Special Issue on Real-time Behavioral Monitoring in IoT Applications using Big Data Analytics. Concurrency and Computation: Practice and Experience. Wiley (2019)
Saufi, S.R., Ahmad, Z.A.B., Leong, M.S., Lim, M.H: Challenges and opportunities of deep learning models for machinery fault detection and diagnosis: a review. IEEE Access 7, 122644–122662 (2019)
Zhang, S., Zhang, S., Wang, B., Habetler, T.G.: Machine learning and deep learning algorithms for bearing fault diagnostics—a comprehensive review (2019)
Jaafar, A.: Vibration analysis and diagnostics guide. College of Engineering, University of Basrah (2012)
Yang, W., Court, R., Jiang. S.: Wind turbine condition monitoring by the approach of SCADA data analysis. Renew. Energy 53 (2013)
Lee, J., Qiu, H., Yu, G., Lin. L.: Bearing data set. NASA ames prognostics data repository, NASA Ames Research Center, Moffett Field, University of Cincinnati (2004)
Saber, M., Saadane, R., Aroussi. H., Chehri, A.: An optimized spectrum sensing implementation based on SVM, KNN and tree algorithms. In: IEEE 15th International Conference on Signal Image Technology & Internet Based Systems, Sorrento, NA, Italy (2019)
Boser, B., Guyon, I., Vapnik, V.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory. ACM, Pittsburgh, Pennsylvania, USA, pp. 144–152 (1992)
Yamada, Y., Suzuki, E., Yokoi, H., Takabayashi, K.: Decision-tree induction from time-series data based on a standard-example splittest. In: Machine Learning, Proceedings of the Twentieth International Conference, Washington, DC, USA (2003)
Kilian, Q., Weinberger, J., Blitzer, J., Saul, L.K.: Distance metric learning for large margin nearest neighbor classification. In: Advances in Neural Information Processing Systems, pp. 1473–1480 (2006)
Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Mach. Learn. 29, 131–163 (1997)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zoungrana, WB., Chehri, A., Zimmermann, A. (2021). Automatic Classification of Rotating Machinery Defects Using Machine Learning (ML) Algorithms. In: Zimmermann, A., Howlett, R., Jain, L. (eds) Human Centred Intelligent Systems. Smart Innovation, Systems and Technologies, vol 189. Springer, Singapore. https://doi.org/10.1007/978-981-15-5784-2_16
Download citation
DOI: https://doi.org/10.1007/978-981-15-5784-2_16
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-5783-5
Online ISBN: 978-981-15-5784-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)