Adaptive Machine Learning Approach for Fault Prognostics based on Normal Conditions - Application to Shaft Bearings of Wind Turbine
Résumé
Prognostics can enhance the reliability and availability of industrial systems while reducing unscheduled faults and maintenance cost. In real industrial systems, data collected from the normal operation conditions of system is available, but there is a lack of historical degradation data is often unavailable. Hence, this paper proposes a general data-driven prognostic approach dealing with the lack of degradation data in the offline phase.
First, features are computed on the collected raw signal, then One Class Support Vector Machine (OCSVM) is used to detect the degradation, this anomaly detection method is trained using only normal operation data. Then, features are ranked according to the selection criteria. The feature having the highest score is chosen as Health Indicator (HI). Finally an adaptive degradation model is applied for the prediction of the degradation evolution over time and Remaining Useful Life (RUL) estimation. The proposed approach is validated using run-to-failure vibration data collected from a high speed shaft bearings of a commercial wind turbine.
Mots clés
raw signal
One Class Support Vector Machine (OCSVM)
classification
degradation
anomaly detection
defect
machine learning
ranking
selection criteria
Health Indicator
structural health monitoring
adaptive degradation model
modelling
degradation evolution
Remaining Useful Life (RUL)
run-to-failure vibration data
high speed shaft bearing
statistical analysis
Prognostics
reliability
availability
unscheduled fault
maintenance cost
data collection
historical degradation data
data-driven prognostic approach
offline
signal processing
Origine : Fichiers produits par l'(les) auteur(s)
Licence : CC BY - Paternité
Licence : CC BY - Paternité