Abstract
Deep learning techniques are gaining popularity due to their ability of feature extraction, dimensionality reduction, and classification. However, one of the biggest challenges in bearing fault diagnosis is reliable feature extraction. When using the bearing fault vibration spectrum, the deep neural network (DNN) model can learn the relationships in data that are unrelated to the task. In this work, a simple approach to bearing fault diagnosis using the elimination of unrelated data artifacts for DNN is proposed. The proposed fault diagnosis pipeline is explained and the comparison with popular fault diagnosis methods is performed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bazurto, A.J., Quispe, E.C., Mendoza, R.C.: Causes and failures classification of industrial electric motor. In: 2016 IEEE ANDESCON, Arequipa, Peru, pp. 1–4, October 2016. https://doi.org/10.1109/ANDESCON.2016.7836190
Nandi, A.K., Ahmed, H.: Condition Monitoring with Vibration Signals: Compressive Sampling and Learning Algorithms for Rotating Machines. Wiley-IEEE Press, Hoboken, NJ, USA (2019)
Sawalhi, N., Randall, R.B., Endo, H.: The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis. Mech. Syst. Sign. Process. 21(6), 2616–2633 (2007). https://doi.org/10.1016/j.ymssp.2006.12.002
Rajeswari, C.: Bearing fault diagnosis using wavelet packet transform, hybrid pso and support vector machine. Procedia Eng. 97, 12 (2014)
Xie, Y., Zhang, T.: A fault diagnosis approach using SVM with data dimension reduction by PCA and LDA method, p. 6
Soualhi, A., Medjaher, K., Zerhouni, N.: Bearing health monitoring based on hilbert-huang transform, support vector machine, and regression. IEEE Trans. Instrum. Meas. 64(1), 52–62 (2015). https://doi.org/10.1109/TIM.2014.2330494
Demetgul, M.: Fault diagnosis on production systems with support vector machine and decision trees algorithms. Int. J. Adv. Manuf. Technol. 12 (2013)
Lee, C.-Y., Huang, Y.K.-Y., Shen, X., Lee, Y.-C.: Improved weighted k-nearest neighbor based on PSO for wind power system state recognition. Energies 13(20), 5520 (2020). https://doi.org/10.3390/en13205520
Lessmeier, C., Kimotho, J.K., Zimmer, D., Sextro, W.: 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, p. 17 (2016)
Maliuk, A.S., Prosvirin, A.E., Ahmad, Z., Kim, C.H., Kim, J.M.: Novel bearing fault diagnosis using Gaussian mixture model-based fault band selection. Sensors 21(19), 6579 (2021). https://doi.org/10.3390/s21196579
Rapur, J.S., Tiwari, R.: Experimental fault diagnosis for known and unseen operating conditions of centrifugal pumps using MSVM and WPT based analyses. Measurement 147, 106809 (2019). https://doi.org/10.1016/j.measurement.2019.07.037
Xu, W.: Research on bearing fault diagnosis base on deep learning. In: 2021 4th International Conference on Artificial Intelligence and Big Data (ICAIBD), Chengdu, China, pp. 261–264, May 2021. https://doi.org/10.1109/ICAIBD51990.2021.9459073
Acknowledgements
This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20192510102510). This work was also supported by the Technology development Program (S3126818) funded by the Ministry of SMEs and Startups (MSS, Korea).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Maliuk, A., Ahmad, Z., Kim, JM. (2022). GMM-Aided DNN Bearing Fault Diagnosis Using Sparse Autoencoder Feature Extraction. In: Pinho, A.J., Georgieva, P., Teixeira, L.F., Sánchez, J.A. (eds) Pattern Recognition and Image Analysis. IbPRIA 2022. Lecture Notes in Computer Science, vol 13256. Springer, Cham. https://doi.org/10.1007/978-3-031-04881-4_44
Download citation
DOI: https://doi.org/10.1007/978-3-031-04881-4_44
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-04880-7
Online ISBN: 978-3-031-04881-4
eBook Packages: Computer ScienceComputer Science (R0)
