Increasing Robustness of Data-Driven Fault Diagnostics with Knowledge Graphs
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
In the realm of PHM, it is common to possess not only process data but also domain knowledge, which, if integrated into data-driven algorithms, can aid in solving specific tasks.
This paper explores the integration of knowledge graphs (KGs) into deep learning models to develop a more resilient approach capable of handling domain shifts, such as variations in machine operation conditions.
We present and assess a KG-enhanced deep learning approach in a representative PHM use case, demonstrating its effectiveness by incorporating domain-invariant knowledge through the KG.
Furthermore, we provide guidance for constructing a comprehensive hierarchical KG representation that preserves semantic information while facilitating numerical representation.
The experimental results showcase the improved performance and domain shift robustness of the KG-enhanced approach in fault diagnostics.
How to Cite
##plugins.themes.bootstrap3.article.details##
Knowledge Graph, Deep Learning, Fault Diagnostics, Transfer Learning, PHM
Chao, M. A., Kulkarni, C., Goebel, K., & Fink, O. (2022). Fusing physics-based and deep learning models for prognostics. Reliability Engineering & System Safety, 217.
Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A simple framework for contrastive learning of visual representations. Proceedings of the International Conference on Machine Learning.
Deng, W., Nguyen, K. T., Gogu, C., Morio, J., & Medjaher, K. (2022). Physics-informed lightweight temporal convolution networks for fault prognostics associated to bearing stiffness degradation. Proceedings of the PHM Society European Conference.
Ding, Y., Zhuang, J., Ding, P., & Jia, M. (2022). Selfsupervised pretraining via contrast learning for intelligent incipient fault detection of bearings. Reliability Engineering & System Safety, 218.
Gebru, T., Hoffman, J., & Fei-Fei, L. (2017). Fine-grained recognition in the wild: A multi-task domain adaptation approach. Proceedings of the IEEE International Conference on Computer Vision.
Hagmeyer, S., Zeiler, P., & Huber, M. F. (2022). On the integration of fundamental knowledge about degradation processes into data-driven diagnostics and prognostics using theory-guided data science. Proceedings of the PHM Society European Conference.
Hogan, A., Blomqvist, E., Cochez, M., díAmato, C., Melo, G. d., Gutierrez, C., . . . others (2021). Knowledge graphs. ACM Computing Surveys, 54(4), 1ñ37.
Jadhav, V., Deodhar, A., Gupta, A., & Runkana, V. (2022). Physics informed neural network for health monitoring of an air preheater. Proceedings of the PHM Society European Conference.
Jain, N., Kalo, J.-C., Balke, W.-T., & Krestel, R. (2021). Do embeddings actually capture knowledge graph semantics? Proceedings of the European Semantic Web Conference.
Jaiswal, A., Babu, A. R., Zadeh, M. Z., Banerjee, D., & Makedon, F. (2020). A survey on contrastive selfsupervised learning. Technologies, 9(1).
Jayathilaka, M., Mu, T., & Sattler, U. (2021). Ontologybased n-ball concept embeddings informing few-shot image classification. Proceedings of the International Conference on Machine Learning and Applications.
Jing, L., Vincent, P., LeCun, Y., & Tian, Y. (2022). Understanding dimensional collapse in contrastive selfsupervised learning. Proceedings of the International Conference on Learning Representations.
Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., . . . Krishnan, D. (2020). Supervised contrastive learning. Proceedings of the Conferences on Neural Information Processing Systems.
Monka, S., Halilaj, L., & Rettinger, A. (2022). A survey on visual transfer learning using knowledge graphs. Semantic Web, 13(3), 477ñ510.
Monka, S., Halilaj, L., Schmid, S., & Rettinger, A. (2021). Learning visual models using a knowledge graph as a trainer. Proceedings of the International Semantic Web Conference.
Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345ñ1359.
Peng, D., Liu, C., & Gryllias, K. (2022). A transfer learningbased rolling bearing fault diagnosis across machines. Annual Conference of the PHM Society.
Rahat, M., Mashhadi, P. S., Nowaczyk, S., Rognvaldsson, T.,
Taheri, A., & Abbasi, A. (2022). Domain adaptation in predicting turbocharger failures using vehicleís sensor measurements. Proceedings of the PHM Society European Conference.
Rombach, K., Michau, G., & Fink, O. (2021). Contrastive learning for fault detection and diagnostics in the context of changing operating conditions and novel fault types. Sensors, 21(10).
Schroff, F., Kalenichenko, D., & Philbin, J. (2015). Facenet: A unified embedding for face recognition and clustering. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
Xia, L., Zheng, P., Li, X., Gao, R. X., & Wang, L. (2022). Toward cognitive predictive maintenance: A survey of graph-based approaches. Journal of Manufacturing Systems, 64, 107ñ120.
Zheng, H., Yang, Y., Yin, J., Li, Y., Wang, R., & Xu, M. (2020). Deep domain generalization combining a priori diagnosis knowledge toward cross-domain fault diagnosis of rolling bearing. IEEE Transactions on Instrumentation and Measurement, 70, 1ñ11.
Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., He, Q. (2020). A comprehensive survey on transfer learning. Proceedings of the IEEE, 109(1), 4376
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.