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
Fault Detection, Fault Classification, Data Challenge
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., & Gagné, 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
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.