Fault Detection and Classification for Robotic Test-bench



Published Jun 29, 2021
Kürşat İnce Uğur Ceylan Nazife Nur Erdoğmuş Engin Sirkeci Yakup Genc


Maintenance of industrial systems often cost as much as their
initial investment. Implementing predictive maintenance via
system health analysis is one of the strategies to reduce maintenance
costs. Health status and life estimation of the machinery
are the most researched topics in this context. In this paper,
we present our analysis for Sixth European Conference of
the Prognostics and Health Management Society 2021 Data
Challenge, which introduces a fuse test bench for qualitycontrol
system, and asks fault detection and classification for
the test bench. We proposed classification workflows, which
deploy gradient boosting, linear discriminant analysis, and
Gaussian process classifiers, and report their performance for
different window sizes. Our gradient boosting based solution
has been ranked 4th in the data challenge.

How to Cite

İnce, K., Ceylan, U., Erdoğmuş, N. N., Sirkeci, E., & Genc, Y. (2021). Fault Detection and Classification for Robotic Test-bench. PHM Society European Conference, 6(1), 7. https://doi.org/10.36001/phme.2021.v6i1.3040
Abstract 35 | PDF Downloads 49



Fault Detection, Fault Classification, Data Challenge

Back, T., Fogel, D. B., & Michalewicz, Z. (2000). Evolutionary computation 1: Basic algorithms and operators (1st ed.). IOP Publishing Ltd.
Chen, T., & Guestrin, C. (2016). Xgboost. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD ’16. doi: https://dx.doi.org/10.1145/2939672.2939785
Erdem, A., & Collot, S. (n.d.). Lofo (leave one feature out) importance. (https://github.com/aerdem4/lofoimportance)
Fortin, F.-A., De Rainville, F.-M., Gardner, M.-A., Parizeau, M., & Gagn´e, C. (2012, jul). DEAP: Evolutionary algorithms made easy. Journal of Machine Learning Research, 13, 2171–2175.
Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5), 1189–1232.
Giordano, D., & Gagar, D. (2021). Sixth european conference of the prognostics and health management society 2021 data challenge. (https://phm-europe.org/)
Heo, S., & Lee, J. H. (2018). Fault detection and classification using artificial neural networks. IFACPapersOnLine, 51(18), 470-475. (10th IFAC Symposium on Advanced Control of Chemical Processes ADCHEM 2018) doi: https://doi.org/10.1016/j.ifacol.2018.09.380
Huang, B., Di, Y., Jin, C., & Lee, J. (2017, 05). Review of data-driven prognostics and health management techniques: Lessons learned from phm data challenge competitions..
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., . . . Liu, T.-Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. In I. Guyon et al. (Eds.), Proceedings of the 31st international conference on neural information processing systems (Vol. 30, p. 3149–3157). Curran Associates Inc.
Matthews, B. (1975, oct). Comparison of the predicted and observed secondary structure of t4 phage lysozyme. Biochimica et Biophysica Acta (BBA) - Protein Structure, 405(2), 442–451. doi: https://doi.org/10.1016/0005-2795(75)90109-9
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., . . . Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
Rasmussen, C. E., & Williams, C. K. I. (2005). Gaussian processes for machine learning (adaptive computation and machine learning). The MIT Press.
Tharwat, A., Gaber, T., Ibrahim, A., & Hassanien, A. E. (2017, 05). Linear discriminant analysis: A detailed tutorial. Ai Communications, 30, 169-190,. doi: https://doi.org/10.3233/AIC-170729
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