Feature Learning for Fault Detection in High-Dimensional
  Condition-Monitoring Signals
  Complex industrial systems are continuously monitored by a large number of
heterogeneous 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 and one-class classification for fault detection
that uses data on healthy conditions only for its training. The approach is
based on stacked Extreme Learning Machines (namely Hierarchical, or HELM) and
comprises an autoencoder, performing unsupervised feature learning, stacked
with a one-class classifier monitoring the distance of the test data to the
training healthy class, thereby assessing the health of the system.
  This study provides a comprehensive evaluation of HELM fault detection
capability compared to other machine learning approaches, such as stand-alone
one-class classifiers (ELM and SVM), these same one-class classifiers combined
with traditional dimensionality reduction methods (PCA) and a Deep Belief
Network. The performance is first evaluated on a synthetic dataset that
encompasses typical characteristics of condition monitoring data. Subsequently,
the approach is evaluated on a real case study of a power plant fault. The
proposed algorithm for fault detection, combining feature learning with the
one-class classifier, demonstrates a better performance, particularly in cases
where condition monitoring data contain several non-informative signals.
