Comparative Study of STFT and 1D CNN for Bearing Fault Detection Based on Vibration Signals

Öz

Bearing fault detection plays a crucial role in ensuring the reliability, safety, and operational efficiency of rotating machinery used in automotive and industrial applications. Early and accurate identification of bearing defects can significantly reduce unexpected downtime, maintenance costs, and potential system failures. Among the numerous diagnostic approaches, vibration signal analysis has emerged as one of the most effective and widely adopted techniques for fault diagnosis, owing to its sensitivity to subtle changes in mechanical behavior. In recent years, deep learning models—particularly Convolutional Neural Networks (CNNs)—have demonstrated remarkable capability in automatically learning complex patterns from raw sensor data. This study conducts a comparative evaluation of two CNN-based methods: a Short-Time Fourier Transform (STFT)-driven two-dimensional (2D) CNN and a raw vibration signal-driven one-dimensional (1D) CNN. The dataset includes vibration signals representing different bearing health states, namely healthy, inner race fault, and outer race fault conditions. The STFT-based 2D CNN performs time–frequency analysis to extract spectral features, while the 1D CNN learns discriminative features directly from time-domain data. Experimental findings reveal that the 1D CNN achieves higher classification accuracy with reduced preprocessing effort, whereas the STFT-based method provides more interpretable visual representations. The comparative results offer practical insights into the trade-offs between interpretability and accuracy, guiding the selection of appropriate models for real-world bearing fault diagnosis systems.

Anahtar Kelimeler

• Bearing fault detection
• Condition monitoring
• Deep learning
• Feature extraction
• Time frequency analysis
• Vibration signals

Kaynakça

1. Lundström, A.; O’Nils, M.: Factory-based vibration data for bearing-fault detection. Data 8(7), 115 (2023). https://doi.org/10. 3390/data8070115
2. Neupane, D.; Seok, J.: Bearing fault detection and diagnosis using case Western Reserve University dataset with deep learning approaches: a review. IEEE Access 8, 93155–93178 (2020). https://doi.org/10.1109/ACCESS.2020.2990528
3. Liang, H.; Cao, J.; Zhao, X.: Average descent rate singular value decomposition and two-dimensional residual neural network for fault diagnosis of rotating machinery. IEEE Trans. Instrum. Meas.71, 1–16 (2022). https://doi.org/10.1109/TIM.2022.3170973
4. Tandon, N., & Choudhury, A. (1999). A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings. *Tribology International*, 32(8), 469–480. https://doi.org/10.1016/S0301-679X(99)00077-8
5. Wu, T., & Liu, C. (2011). An improved Hilbert–Huang transform and its application in vibration signal analysis. *Journal of Sound and Vibration*, 295(3–5), 953–974. https://doi.org/10.1016/j.jsv.2006.01.020
6. Janssens, O., Van de Walle, R., Loccufier, M., & Van Hoecke, S. (2016). Deep learning for infrared thermal image based machine health monitoring. *IEEE/ASME Transactions on Mechatronics*, 23(1), 151–159. https://doi.org/10.1109/TMECH.2017.2765683
7. Zhao, R., Yan, R., Wang, J., Mao, K., & Shen, F. (2017). Learning to monitor machine health with convolutional and recurrent neural networks. *IEEE Transactions on Industrial Informatics*, 14(9), 4334–4343. https://doi.org/10.1109/TII.2017.2788802
8. Christian Lessmeier1 , James Kuria Kimotho2 , Detmar Zimmer3 and Walter Sextro ,Condition Monitoring of Bearing Damage in Electromechanical Drive Systems by Using Motor Current Signals of Electric Motors: A Benchmark Data Set for Data-Driven Classification https://doi.org/10.36001/phme.2016.v3i1.1577

9. Allen, J. B., & Rabiner, L. R. (1977). A unified approach to short-time Fourier analysis and synthesis. Proceedings of the IEEE, 65(11), 1558–1564. https://doi.org/10.1109/PROC.1977.10770
10. https://en.wikipedia.org/wiki/Short-time_Fourier_transform