  Vibration-based techniques are among the most common condition monitoring
approaches. With the advancement of computers, these approaches have also been
improved such that recently, these approaches in conjunction with deep learning
methods attract attention among researchers. This is mostly due to the nature
of the deep learning method that could facilitate the monitoring procedure by
integrating the feature extraction, feature selection, and classification steps
into one automated step. However, this can be achieved at the expense of
challenges in designing the architecture of a deep learner, tuning its
hyper-parameters. Moreover, it sometimes gives low generalization capability.
As a remedy to these problems, this study proposes a framework based on
ensemble deep learning methodology. The framework was initiated by creating a
pool of Convolutional neural networks (CNN). To create diversity to the CNNs,
they are fed by frequency responses which are passed through different
functions. As the next step, proper CNNs are selected based on an information
criterion to be used for fusion. The fusion is then carried out by improved
Dempster-Shafer theory. The proposed framework is applied to real test data
collected from Equiax Polycrystalline Nickel alloy first-stage turbine blades
with complex geometry.
