Author(s)

Binbin Qiu ^1,2, Siqi Liu ¹, Yu Zhu ¹, Chunhua Feng ¹

Affiliation(s)

¹ School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
² Department of Production Engineering, KTH Royal Institute of Technology, Stockholm, Sweden

Corresponding Author

Binbin Qiu

ABSTRACT

With the development of modern educational technology, the teaching of vibration testing and signal analysis techniques faces new challenges. To enhance teaching effectiveness and improve students' understanding of complex theories, this paper proposes a teaching case for bearing fault diagnosis based on continuous wavelet transform and CNN-BiLSTM. First, by utilizing wavelet transform for time-frequency analysis of vibration signals, students can gain a deeper understanding of the core principles of signal processing. Second, the introduction of the CNN-BiLSTM model in deep learning enables students to not only grasp the fundamental concepts of deep learning but also enhance their problem-solving abilities in practical engineering scenarios. Experimental results indicate that this approach can effectively improve students' mastery of signal analysis and fault diagnosis techniques, showing significant advantages in fostering innovative thinking and practical skills. This study provides new ideas and practical cases for the reform of teaching vibration testing and signal analysis techniques.

KEYWORDS

Vibration Testing and Signal Analysis Techniques, Continuous Wavelet Transform, CNN-BiLSTM, Bearing Fault Diagnosis

CITE THIS PAPER

Binbin Qiu, Siqi Liu, Yu Zhu, Chunhua Feng, Case Study Analysis of Rotating Machinery Fault Diagnosis for Course on Vibration Testing and Signal Analysis Techniques. Curriculum and Teaching Methodology (2025) Vol. 8: 1-8. DOI: http://dx.doi.org/10.23977/curtm.2025.080301.

REFERENCES

[1] Ruonan L., Boyuan Y., Enrico Z., Xuefeng C.  (2018) Artificial intelligence for fault diagnosis of rotating machinery: A review, Mechanical Systems and Signal Processing, 108, 33-47.
[2] Dongdong L., Lingli C., Weidong C. (2024) A Review on Deep Learning in Planetary Gearbox Health State Recognition: Methods, Applications, and Dataset Publication [J], Measurement Science And Technology, 35(1).
[3] R. Balamurugan, D.G. Takale, M.M. Parvez, S. Gnanamurugan. (2024) A novel prediction of remaining useful life time of rolling bearings using convolutional neural network with bidirectional long short term memory, Journal of Engineering Research.
[4] Sun, H., He, Z., Zi, Y., Yuan, J., Wang, X., Chen, J., & He, S. (2014) Multiwavelet transform and its applications in mechanical fault diagnosis – A review. Mechanical Systems and Signal Processing, 43, 1-24.
[5] Deng, L., Zhao, C., Yan, X., Zhang, Y., & Qiu, R. (2025) A novel approach for bearing fault diagnosis in complex environments using PSO-CWT and SA-FPN. Measurement.

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