Interpreting What Typical Fault Signals Look Like via Prototype-matching
Qian Chen Xingjian Dong Zhike Peng
Neural networks, with powerful nonlinear mapping and classification
capabilities, are widely applied in mechanical fault diagnosis to ensure
safety. However, being typical black-box models, their application is limited
in high-reliability-required scenarios. To understand the classification logic
and explain what typical fault signals look like, the prototype matching
network (PMN) is proposed by combining the human-inherent prototype-matching
with autoencoder (AE). The PMN matches AE-extracted feature with each prototype
and selects the most similar prototype as the prediction result. It has three
interpreting paths on classification logic, fault prototypes, and matching
contributions. Conventional diagnosis and domain generalization experiments
demonstrate its competitive diagnostic performance and distinguished advantages
in representation learning. Besides, the learned typical fault signals (i.e.,
sample-level prototypes) showcase the ability for denoising and extracting
subtle key features that experts find challenging to capture. This ability
broadens human understanding and provides a promising solution from
interpretability research to AI-for-Science.