Evaluation of the Use of the Angular Domain and Order Domain in a Bearing Fault Detection Framework using Deep Learning

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Published Nov 5, 2024
Racquel Knust Domingues Julio A. Cordioli Danilo Silva Danilo de Souza Braga Guilherme Cartagena Miron

Abstract

Bearing failures are very common in the industrial environment, requiring effective fault detection methods, which can be categorized into physics-based, knowledge-based and data-driven types. Data-driven methods are efficient in differentiating healthy conditions from faulty conditions by characterizing machine signals, involving stages of data acquisition, feature extraction, and condition determination. Traditionally, feature extraction and condition determination were manual, but advances in artificial intelligence and machine learning, especially deep learning, have automated this process. Although deep learning automatically learns the best features from the input data, the signal domain can influence the model's performance. Time and frequency domain representations are widely used in fault detection methodologies using vibration signals, while angular and order domains are more common in variable operating conditions, but direct use of these domains with deep learning is still rare in the literature. Considering this, this study evaluates a bearing fault detection methodology using vibration signals in different domains (time, frequency, angular, and order) under various rotational conditions. Three distinct approaches were tested to assess the effectiveness of these representations. The results indicated that the frequency domain representation had the best overall performance and the study concluded that the angular and order domains do not offer significant advantages compared to the frequency domain. Nonetheless, it is recommended to conduct a more in-depth analysis with more diverse datasets, especially those containing early-stage bearing fault signals.

How to Cite

Knust Domingues, R., Cordioli, J. A., Silva, D., Braga, D. de S., & Miron, G. C. (2024). Evaluation of the Use of the Angular Domain and Order Domain in a Bearing Fault Detection Framework using Deep Learning. Annual Conference of the PHM Society, 16(1). https://doi.org/10.36001/phmconf.2024.v16i1.4146
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Keywords

Bearing Fault Detection, Deep Learning, Angle and Order Domain, Vibration Signals

References

Borghesani, P., Pennacchi, P., Chatterton, S., & Ricci, R. (2014). The velocity synchronous discrete fourier transform for order tracking in the field of rotating machinery. Mechanical Systems and Signal Processing, 44(1-2), 118–133.

Domingues, R. K. (2023). Avaliaco de metodologia de detecco de falhas em mancais de rolamento utilizando anLalise de ordem e mLedia sLıncrona no tempo (Masters dissertation). Federal University of Santa Catarina, FlorianLopolis.

GLeron, A. (2022). Hands-on machine learning with scikitlearn, keras, and tensorflow. ” O’Reilly Media, Inc.”. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press. (http://www.deeplearningbook.org)

Guyon, I., & Elisseeff, A. (2006). An introduction to feature extraction. In I. Guyon, M. Nikravesh, S. Gunn, & L. A. Zadeh (Eds.), Feature extraction: Foundations and applications (pp. 1–25). Berlin, Heidelberg: Springer Berlin Heidelberg. doi: 10.1007/978-3-540- 35488-81

He, C., Cao, Y., Yang, Y., Liu, Y., Liu, X., & Cao, Z. (2023). Fault diagnosis of rotating machinery based on the improved multidimensional normalization resnet. IEEE Transactions on Instrumentation and Measurement, 72. doi: 10.1109/TIM.2023.3293554

Hoang, D.-T., & Kang, H.-J. (2019). A survey on deep learning based bearing fault diagnosis. Neurocomputing, 335, 327–335.

Huang, H., & Baddour, N. (2019). Bearing vibration data under time-varying rotational speed conditions. Mende- ley Data. doi: 10.17632/v43hmbwxpm.2

Huang, X., Zhang, J., Xu, Z., & Xu, S. (2024). Fault diagnosis of motor bearings with multiple time frequency extraction method under variable speed conditions. In 2024 10th international power electronics and motion control conference (ipemc 2024-ecce asia). Nanjing, China: IEEE. doi: 10.1109/IPEMCECCEAsia60879.2024.10567995

Lei, Y. (2016). Intelligent fault diagnosis and remaining useful
life prediction of rotating machinery. Butterworth- Heinemann.

Lu, S., Yan, R., Liu, Y., & Wang, Q. (2019). Tacholess speed estimation in order tracking: A review with application to rotating machine fault diagnosis. IEEE Transactions on Instrumentation and Measurement, 68(7), 2315–2332.

Mushtaq, S., Islam, M. M., & Sohaib, M. (2021). Deep learning aided data-driven fault diagnosis of rotatory machine: A comprehensive review. Energies, 14(16), 5150. Peng, B., Bi, Y., Xue, B., Zhang, M., & Wan, S. (2022).

A survey on fault diagnosis of rolling bearings. Algorithms, 15(10), 347. Randall, R. B. (2021). Vibration-based condition monitoring: industrial, automotive and aerospace applications. John Wiley & Sons.

RLemond, D., Antoni, J., & Randall, R. (2014). Editorial for the special issue on instantaneous angular speed (ias) processing and angular applications (Vol. 44) (No. 1- 2). Elsevier.

Rosa, R. K., Borges, V. K., de S. Braga, D., & Silva, D. (2023 doi: 10.14209/sbrt.2023.1570915812

Shin, K., & Hammond, J. (2008). Fundamentals of signal processing for sound and vibration engineers. John Wiley & Sons.

Wang, K., Huang, Y., Zhang, B., Luo, H., Yu, X., Chen, D., & Zhang, Z. (2024). Improved synchronous sampling and its application in high-speed railway bearing damage detection. Machines, 12(2), 101. doi: 10.3390/machines12020101

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. doi: 10.1109/ACCESS.2020.2972859

Zhang,W., Peng, G., Li, C., Chen, Y., & Zhang, Z. (2017). A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals. Sensors, 17(2), 425.

Zhang, X., Zhao, B., & Lin, Y. (2021). Machine learning based bearing fault diagnosis using the case western reserve university data: A review. Ieee Access, 9, 155598–155608.
Section
Technical Research Papers