Condition-based maintenance aims to determine the machine state in real time, by monitoring the signals it emits. Such signals are potentially unlimited, generated at a high rate, and can evolve over time. These conditions tend to produce changes in the distribution of the data, known as concept drift. This phenomenon is analyzed and used to establish changes in the state of the machine. The present article proposes a methodological framework for the diagnosis of fault severity based on concept drift. A parsimonious unsupervised algorithm based on KNN is proposed to detect concept evolution. The results show that the algorithm is quite effective in declaring a concept evolution that is associated with a change in the failure condition of the machine. Finally, the results show that there is a high correlation between the displacement of the centroids of the emerging concepts and the % of deterioration of the machine.
|Title of host publication||ETCM 2021 - 5th Ecuador Technical Chapters Meeting|
|Editors||Monica Karel Huerta, Sebastian Quevedo, Carlos Monsalve|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|State||Published - 12 Oct 2021|
|Event||5th IEEE Ecuador Technical Chapters Meeting, ETCM 2021 - Cuenca, Ecuador|
Duration: 12 Oct 2021 → 15 Oct 2021
|Name||ETCM 2021 - 5th Ecuador Technical Chapters Meeting|
|Conference||5th IEEE Ecuador Technical Chapters Meeting, ETCM 2021|
|Period||12/10/21 → 15/10/21|
Bibliographical noteFunding Information:
ACKNOWLEDGMENT This paper is supported by the research group GIDTEC of the Universidad Politécnica Salesiana, sede Cuenca-Ecuador and the LIDI Computer Science Research Institute of the Universidad Nacional de La Plata.
© 2021 IEEE.
- Concept drift
- concept evolution
- fault detection
- Jensen-Shannon divergence
- Kullback-Leibler divergence