  Nowadays, using vibration data in conjunction with pattern recognition
methods is one of the most common fault detection strategies for structures.
However, their performances depend on the features extracted from vibration
data, the features selected to train the classifier, and the classifier used
for pattern recognition. Deep learning facilitates the fault detection
procedure by automating the feature extraction and selection, and
classification procedure. Though, deep learning approaches have challenges in
designing its structure and tuning its hyperparameters, which may result in a
low generalization capability. Therefore, this study proposes an ensemble deep
learning framework based on a convolutional neural network (CNN) and
Dempster-Shafer theory (DST), called CNN-DST. In this framework, several CNNs
with the proposed structure are first trained, and then, the outputs of the
CNNs selected by the proposed technique are combined by using an improved
DST-based method. To validate the proposed CNN-DST framework, it is applied to
an experimental dataset created by the broadband vibrational responses of
polycrystalline Nickel alloy first-stage turbine blades with different types
and severities of damage. Through statistical analysis, it is shown that the
proposed CNN-DST framework classifies the turbine blades with an average
prediction accuracy of 97.19%. The proposed CNN-DST framework is benchmarked
with other state-of-the-art classification methods, demonstrating its high
performance. The robustness of the proposed CNN-DST framework with respect to
measurement noise is investigated, showing its high noise-resistance. Further,
bandwidth analysis reveals that most of the required information for detecting
faulty samples is available in a small frequency range.
