Unknown Health States Recognition With Collective Decision Based Deep
  Learning Networks In Predictive Maintenance Applications
Chuyue Lou M. Amine Atoui
At present, decision making solutions developed based on deep learning (DL)
models have received extensive attention in predictive maintenance (PM)
applications along with the rapid improvement of computing power. Relying on
the superior properties of shared weights and spatial pooling, Convolutional
Neural Network (CNN) can learn effective representations of health states from
industrial data. Many developed CNN-based schemes, such as advanced CNNs that
introduce residual learning and multi-scale learning, have shown good
performance in health state recognition tasks under the assumption that all the
classes are known. However, these schemes have no ability to deal with new
abnormal samples that belong to state classes not part of the training set. In
this paper, a collective decision framework for different CNNs is proposed. It
is based on a One-vs-Rest network (OVRN) to simultaneously achieve
classification of known and unknown health states. OVRN learn state-specific
discriminative features and enhance the ability to reject new abnormal samples
incorporated to different CNNs. According to the validation results on the
public dataset of Tennessee Eastman Process (TEP), the proposed CNN-based
decision schemes incorporating OVRN have outstanding recognition ability for
samples of unknown heath states, while maintaining satisfactory accuracy on
known states. The results show that the new DL framework outperforms
conventional CNNs, and the one based on residual and multi-scale learning has
the best overall performance.