An interpretable deep learning method for bearing fault diagnosis
Hao Lu Austin M. Bray Chao Hu Andrew T. Zimmerman Hongyi Xu
Deep learning (DL) has gained popularity in recent years as an effective tool
for classifying the current health and predicting the future of industrial
equipment. However, most DL models have black-box components with an underlying
structure that is too complex to be interpreted and explained to human users.
This presents significant challenges when deploying these models for
safety-critical maintenance tasks, where non-technical personnel often need to
have complete trust in the recommendations these models give. To address these
challenges, we utilize a convolutional neural network (CNN) with
Gradient-weighted Class Activation Mapping (Grad-CAM) activation map
visualizations to form an interpretable DL method for classifying bearing
faults. After the model training process, we apply Grad-CAM to identify a
training sample's feature importance and to form a library of diagnosis
knowledge (or health library) containing training samples with annotated
feature maps. During the model evaluation process, the proposed approach
retrieves prediction basis samples from the health library according to the
similarity of the feature importance. The proposed method can be easily applied
to any CNN model without modifying the model architecture, and our experimental
results show that this method can select prediction basis samples that are
intuitively and physically meaningful, improving the model's trustworthiness
for human users.