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 434 | PDF Downloads 359



Fault Detection, Fault Classification, Data Challenge

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