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].
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