Feature learning for fault detection in high-dimensional condition-monitoring signals
Metadata only
Date
2018-10-12Type
- Working Paper
ETH Bibliography
yes
Altmetrics
Abstract
Complex industrial systems are continuously monitored by a large number of heterogenous sensors. The diversity of their operating conditions and the possible fault types make it impossible to collect enough data for learning all the possible fault patterns.
The paper proposes an integrated automatic unsupervised feature learning approach for fault detection that uses healthy conditions data only for its training. The approach is based on stacked Extreme Learning Machines (namely Hierarchical, or HELM) and comprises stacked autoencoders performing unsupervised feature learning, and a one-class classifier monitoring the variations in the features to assess the health of the system.
This study provides a comprehensive evaluation of HELM fault detection capability compared to other machine learning approaches, including Deep Belief Networks. The performance is first evaluated on a synthetic dataset with typical characteristics of condition monitoring data. Subsequently, the approach is evaluated on a real case study of a power plant fault.
HELM demonstrates a better performance specifically in cases where several non-informative signals are included. Show more
Publication status
publishedExternal links
Journal / series
arXivPages / Article No.
Publisher
Cornell UniversityOrganisational unit
09642 - Fink, Olga (ehemalig) / Fink, Olga (former)
Related publications and datasets
Is previous version of: http://hdl.handle.net/20.500.11850/364989
More
Show all metadata
ETH Bibliography
yes
Altmetrics