Fault Diagnosis of Rolling Bearings Using Spectral Peaks and Neural Networks
DOI:
https://doi.org/10.14529/cmse220205Keywords:
fault diagnosis, rolling bearing, signal spectrum, neural networks, t-SNEAbstract
The most important components of machine parts are rolling bearings, the condition of which is necessary to control, since possible defects in their design can lead to incorrect operation or general failure of machines. Modern solutions on fault diagnosis of bearings typically use complex feature extraction processes, such as their Hilbert spectrum imaging and a further powerful neural network to classify them. In this article, we propose a simple, but, nevertheless, an effective algorithm for solving this problem. To extract features from a signal, we divide the signal spectrum into equal subintervals and find the amplitude maximum and the corresponding frequency value in each of them. In the article, based on the t-SNE method, it is shown that the features selected in this way, despite their small size, represent different types of signals well. At the second stage, the selected features are fed to the input of a simple classifier neural network. The proposed method is computationally simple, both at the stage of feature extraction and at the stage of neural network training. Despite this, the method gives 100% accuracy for all types of signals on short data from the IMS dataset.References
Yu X., Dong F., Ding E., et al. Rolling Bearing Fault Diagnosis Using Modified LFDA and EMD With Sensitive Feature Selection. IEEE Access. 2018. Vol. 6. P. 3715–3730. DOI: 10.1109/ACCESS.2017.2773460.
Xu Y., Li Z., Wang S., et al. A hybrid deep-learning model for fault diagnosis of rolling bearings. Measurement. 2021. Vol. 169. P. 108502. DOI: 10.1016/j.measurement.2020.108502.
Han H., Wang H., Liu Z., Wang J. Intelligent vibration signal denoising method based on non-local fully convolutional neural network for rolling bearings. ISA Transactions. 2022. Vol. 122. P. 13–23. DOI: 10.1016/j.isatra.2021.04.022.
Hamad R., Jarpe E., Lundstrom J. Stability Analysis of the t-SNE Algorithm for Human Activity Pattern Data. Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. 2019. P. 1839–1845. DOI: 10.1109/SMC.2018.00318.
Prognostics Center of Excellence - Data Repository. URL: https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/#bearing (accessed: 21.01.2022).
Wang W., Ismail F., et al. An enhanced bispectrum technique with auxiliary frequency injection for induction motor health condition monitoring. IEEE Transactions on Instrumentation and Measurement. 2015. Vol. 64, no. 10. P. 2679–2687. DOI: 10.1109/TIM.2015.2419031.
Zhen L., Zhengjia H., Yanyang Z., Xuefeng C. Bearing condition monitoring based on shock pulse method and improved redundant lifting scheme. Mathematics and Computers in Simulation. 2008. Vol. 79, no. 3. P. 318–338. DOI: 10.1016/j.matcom.2007.12.004.
Wang W., McFadden P. Early detection of gear failure by vibration analysis i. calculation of the time-frequency distribution. Mechanical Systems and Signal Processing. 1993. Vol. 7, no. 3. P. 193–203. DOI: 10.1006/mssp.1993.1008.
Luo G.Y., Osypiw D., Irle M. On-line vibration analysis with fast continuous wavelet algorithm for condition monitoring of bearing. Journal of vibration and control. 2003. Vol. 9, no. 8. P. 931–947.
Lei Y., Lin J., He Z., Zuo M.J. A review on empirical mode decomposition in fault diagnosis of rotating machinery. Mechanical Systems and Signal Processing. 2013. Vol. 35, no. 1-2. P. 108–126. DOI: 10.1016/j.ymssp.2012.09.015.
Yuan Z., Zhang L., Duan L., Li T. Intelligent fault diagnosis of rolling element bearings based on HHT and CNN. 2018 Prognostics and System Health Management Conference (PHM-Chongqing). IEEE, 2018. P. 292–296.
Liang P., Deng C.,Wu J., et al. Intelligent Fault Diagnosis of Rolling Element Bearing Based on Convolutional Neural Network and Frequency Spectrograms. 2019 IEEE International Conference on Prognostics and Health Management (ICPHM). IEEE, 2019. P. 1–5. DOI: 10.1109/ICPHM.2019.8819444.
Shao H., Jiang H., Zhao H., Wang F. A novel deep autoencoder feature learning method for rotating machinery fault diagnosis. Mechanical Systems and Signal Processing. 2017. Vol. C, no. 95. P. 187–204. DOI: 10.1016/J.YMSSP.2017.03.034.
Liu H., Zhou J., Zheng Y., et al. Fault diagnosis of rolling bearings with recurrent neural network-based autoencoders. ISA transactions. 2018. Vol. 77. P. 167–178. DOI: 10.1016/J.ISATRA.2018.04.005.
Xu Z., Li C., Yang Y. Fault diagnosis of rolling bearing of wind turbines based on the Variational Mode Decomposition and Deep Convolutional Neural Networks. Applied Soft Computing. 2020. Vol. 95. P. 106515. DOI: 10.1016/J.ASOC.2020.106515.
Chen Z., Cen J., Xiong J. Rolling Bearing Fault Diagnosis Using Time-Frequency Analysis and Deep Transfer Convolutional Neural Network. IEEE Access. 2020. Vol. 8. P. 150248–150261. DOI: 10.1109/ACCESS.2020.3016888.
Zhang W., Peng G., Li C., et al. A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals. Sensors. 2017. Vol. 17, no. 2. P. 425. DOI: 10.3390/s17020425.
Li X., Wang J., Zhang B. Fault diagnosis of rolling element bearing weak fault based on sparse decomposition and broad learning network. Transactions of the Institute of Measurement and Control. 2020. Vol. 42, no. 2. P. 169–179. DOI: 10.1177/0142331219864820.
Zhang R., Peng Z., Wu L., et al. Fault Diagnosis from Raw Sensor Data Using Deep Neural Networks Considering Temporal Coherence. Sensors. 2017. Vol. 17, no. 3. P. 549. DOI: 10.3390/S17030549.
