Development of Fault Diagnosis Model based on Semi-supervised Autoencoder

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Published Jun 27, 2024
Yongjae Jeon Kyumin Kim Yelim Lee Byeong Kwon Kang Sang Won Lee

Abstract

The maintenance paradigm based on PHM (Prognostics and Health Management) technology, utilizing big data to predict process conditions through manufacturing intelligence, is rising. However, in most industries, there is lack of accurate labeling of sensor data, posing challenges in data utilization due to the significant cost of labeling tasks. Consequently, recent research has focused on semi-supervised learning methodologies, which are applicable in label-absent scenarios. Especially, there is a growing emphasis on semi-supervised autoencoder, which learns both labeled and unlabeled data simultaneously. Also, there is a demand for the development of fault diagnosis models for essential components, such as bearings in most mechanical systems. Vibrational data is actively being integrated with artificial intelligence for application in bearing fault diagnosis frameworks. Nonetheless, diagnosing the condition of bearings inside machine systems, especially within the machine tool spindle, remains challenging, as the labeling of collected data causes significant costs. Therefore, this paper aims to develop a fault diagnosis model for unlabeled bearings in machine tool spindle using a semi-supervised autoencoder. Initially, a monitoring system of bearing simulator that imitates a machine tool spindle bearing was constructed, and vibration signals from both normal and fault bearings were collected based on this system. Subsequently, a semi-supervised autoencoder model was developed to construct a fault diagnosis model using labeled simulator data and unlabeled machine tool spindle bearing data. To evaluate the model, additional data of normal and fault bearings in machine tool spindle were collected, and the performance of the model was compared with a conventional fault diagnosis model based on 1D-CNN.

How to Cite

Jeon, Y., Kim, K., Lee, Y., Kang, B. K., & Lee, S. W. (2024). Development of Fault Diagnosis Model based on Semi-supervised Autoencoder. PHM Society European Conference, 8(1), 7. https://doi.org/10.36001/phme.2024.v8i1.4023
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Keywords

Semi-supervised Autoencoder, Fault Diagnosis, Bearing

References
Wen, L., Li, X., Gao, L., & Zhang, Y. (2017). A new convolutional neural network-based data-driven fault diagnosis method. IEEE Transactions on Industrial Electronics, 65(7), 5990-5998. doi: 10.1109/TIE.2017.2774777

Ince, T., Kiranyaz, S., Eren, L., Askar, M., & Gabbouj, M. (2016). Real-time motor fault detection by 1-D convolutional neural networks. IEEE Transactions on Industrial Electronics, 63(11), 7067-7075. doi: 10.1109/TIE.2016.2582729

Cabrera, D., Guamán, A., Zhang, S., Cerrada, M., Sanchez, R. V., Cevallos, J., Long, J. & Li, C. (2020). Bayesian approach and time series dimensionality reduction to LSTM-based model-building for fault diagnosis of a reciprocating compressor. Neurocomputing, 380, 51-66. doi: https://doi.org/10.1016/j.neucom.2019.11.006

Abed, W., Sharma, S., Sutton, R., & Motwani, A. (2015). A robust bearing fault detection and diagnosis technique for brushless DC motors under non-stationary operating conditions. Journal of Control, Automation and Electrical Systems, 26, 241-254. doi: https://doi.org/10.1007/s40313-015-0173-7

Janssens, O., Slavkovikj, V., Vervisch, B., Stockman, K., Loccufier, M., Verstockt, S., Walle, R. V. & Hoecke, S. V. (2016). Convolutional neural network based fault detection for rotating machinery. Journal of Sound and Vibration, 377, 331-345. doi: https://doi.org/10.1016/j.jsv.2016.05.027

Lu, C., Wang, Z., & Zhou, B. (2017). Intelligent fault diagnosis of rolling bearing using hierarchical convolutional network based health state classification. Advanced Engineering Informatics, 32, 139-151. doi: https://doi.org/10.1016/j.aei.2017.02.005

Jiang, H., Li, X., Shao, H., & Zhao, K. (2018). Intelligent fault diagnosis of rolling bearings using an improved deep recurrent neural network. Measurement Science and Technology, 29(6), 065107. doi: 10.1088/13616501/aab945

Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. science, 313(5786), 504-507. doi: 10.1126/science.1127647

Kingma, D. P., & Welling, M. (2013). Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114. doi: https://doi.org/10.48550/arXiv.1312.6114

Sakurada, M., & Yairi, T. (2014). Anomaly detection using autoencoders with nonlinear dimensionality reduction. In Proceedings of the MLSDA 2014 2nd workshop on machine learning for sensory data analysis (pp. 4-11). doi: https://doi.org/10.1145/2689746.2689747

Reddy, Y. C. A. P., Viswanath, P., & Reddy, B. E. (2018). Semi-supervised learning: A brief review. International Journal of Engineering &Technology, 7(1.8) (pp. 81-85). doi: https://doi.org/10.14419/ijet.v7i1.8.9977
Section
Technical Papers