Bearing faults diagnosis using envelope analysis and 1D Convolutional neuralnetwork
DOI:
https://doi.org/10.5281/zenodo.8070891Keywords:
Rolling bearing, Hilbert transform, envelope analysis, 1D convolutional neural networkAbstract
Due to the importance of rolling bearings as one of the most widely used industrial machinery elements. Therefore, the development of a method to monitor the condition of bearing is very important. This work presents a novel method to classify the bearing faults by using an envelope analysis and 1D-CNN. Firstly, envelope analysis is used as a method for pre-processing by calculating the envelope spectrum of the raw vibration data. Secondly, a 1D-CNN is used as a classifier to diagnose the bearing faults. The proposed method is tested on the CWRU dataset from bearings under different rotating speeds. Results of the case study show that the proposed method can achieve a testing accuracy of 99.85 %.
References
B. Bengherbia, R. Kara, A. Toubal, M. Ould Zmirli, S. Chadli, and P. Wira, “FPGA implementation of a wireless sensor node with a built-in ADALINE neural network coprocessor for vibration analysis and fault diagnosis in machine condition monitoring,” Measurement, vol. 163, p. 107960, 2020.
G. Georgoulas, T. Loutas, C. D. Stylios, and V. Kostopoulos, “Bearing fault detection based on hybrid ensemble detector and empirical mode decomposition,” Mech. Syst. Signal Process., vol. 41, no. 1–2, pp. 510–525, 2013.
Y. Toumi, B. Bengherbia, S. Lachenani, and M. Ould Zmirli, “FPGA Implementation of a Bearing Fault Classification System Based on an Envelope Analysis and Artificial Neural Network,” Arab. J. Sci. Eng., 2022.
S. Tyagi and S. K. Panigrahi, “An improved envelope detection method using particle swarm optimisation for rolling element bearing fault diagnosis,” J. Comput. Des. Eng., vol. 4, no. 4, pp. 305–317, 2017.
S. A. McInerny and Y. Dai, “Basic vibration signal processing for bearing fault detection,” IEEE Trans. Educ., vol. 46, no. 1, pp. 149–156, 2003.
S. L. Marple, “Computing the Discrete-Time ‘Analytic’ Signal via FFT,” IEEE Trans. SIGNAL Process., vol. 47, no. 9, pp. 2600–2603, 1999.
“Bearing Data Center | Case School of Engineering | Case Western Reserve University.” [Online]. Available: https://engineering.case.edu/bearingdatacenter. [Accessed: 04-Apr-2022].
Published
Issue
Section
License
Copyright (c) 2022 AINTELIA Science Notes Journal
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
COPYRIGHT NOTICE
Authors submitting a manuscript do so on the understanding that if accepted for publication, copyright of the article shall be assigned to Aintelia® Science Notes Journal (ASNJ).
By submitting their work, authors agree to the following terms:
-
Copyright Transfer: Copyright of the published article is transferred to Aintelia® Science Notes Journal. The journal reserves the right to publish, reproduce, distribute, and archive the work.
-
Licensing: While the journal retains the copyright, the article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). This allows third parties to share and adapt the work for non-commercial purposes, provided the original work and the journal are properly cited.
-
Author Rights: Authors retain the right to use their article for their own scholarly needs, such as including it in a thesis or dissertation, presenting it at conferences, or distributing it to students for educational purposes, provided that the journal is cited as the original publisher.
