Development of Fault Diagnosis Model based on Semi-supervised Autoencoder
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
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
##plugins.themes.bootstrap3.article.details##
Semi-supervised Autoencoder, Fault Diagnosis, Bearing
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
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.