Domain Adaptation based Fault Diagnosis under Variable Operating Conditions of a Rock Drill

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Jul 23, 2023
Yong Chae Kim
Taehun Kim
Jin Uk Ko
Jinwook Lee
Keon Kim

Abstract

Data-driven fault diagnosis is an essential technology for the safety and maintenance of rock drills. However, since the signals acquired from a rock drill have different distributions, which arise due to their variable operating conditions, the classification performance of any data-driven method is diminished; this is called the domain-shift issue. This paper proposes a new domain-adaptation-based fault diagnosis scheme to solve the domain-shift problem. The proposed method introduces a data-cropping technique to mitigate the difference in the length of the data measured from a rock drill for each impact cycle. To extract invariant features for all operating conditions, the proposed method combines two methods: a domain adversarial neural network and minimization of the maximum mean discrepancy (MMD) between the features from different domains. In addition, a soft voting ensemble is used to reduce the model uncertainty. The proposed method shows superior performance when validated with a rock drill dataset; the proposed approach was ranked in 2nd place in the 2022 PHM Conference Data Challenge.

Abstract 357 | PDF Downloads 452

##plugins.themes.bootstrap3.article.details##

Keywords

fault diagnosis, rock drill, deep-learning, domain-adaptation

References
Erik, J, Erik, F., Mattias, K., & Robert, P. (2021). Fault Identification in Hydraulic Rock Drills from Indirect Measurement During Operation. IFAC-PapersOnLine, vol. 54(11), pp. 73-78. doi.org/10.1016/j.ifacol.2021.10.053
Li, B., Chow, MY., Tipsuwan, Y., & Hung, J.C. (2000) Neural-network-based motor rolling bearing fault diagnosis. IEEE transactions on industrial electronics, vol.47(5), pp. 1060-1069. doi.org/10.1109/41.873214
Guo, X., Chen, L., & Shen, C. (2016). Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis. Measurement, vol. 93, pp. 490-502. doi.org/10.1016/j.measurement.2016.07.054
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., & Lempitsky, V. (2016). Domain-adversarial training of neural networks. The journal of machine learning research, vol.17(1), pp. 2096-2030. doi.org/10.48550/arXiv.1505.07818
Jiao, J., Zhao, M., & Lin, J. (2019). Unsupervised adversarial adaptation network for intelligent fault diagnosis. IEEE Transactions on Industrial Electronics, vol. 67(11), pp. 9904-9913. doi.org/10.1109/TIE.2019.2956366
Guo, L., Lei, Y., Xing, S., Tan, T., & Li, N. (2018). Deep convolutional transfer learning network: A new method for intelligent fault diagnosis of machines with unlabeled data. IEEE Transactions on Industrial Electronics, vol.66(9) pp. 7316-7325. doi.org/10.1109/TIE.2018.2877090
Borgwardt, KM., Gretton, A., Rasch, MJ., Kriegel, HP., Schölkopf, B., Karsten M., & Smola, AJ. (2006) Integrating structured biological data by kernel maximum mean discrepancy. Bioinformatics, vol.22(14) pp. 49-57. doi.org/10.1093/bioinformatics/btl242
Van der Maaten, L., & Hinton, G., (2008). Visualizing data using t-SNE. Journal of machine learning research, 9 (11).
Section
Technical Papers