CS-SHAP: Extending SHAP to Cyclic-Spectral Domain for Better Interpretability of Intelligent Fault Diagnosis
Journal:
arXiv
Published Date:
Feb 10, 2025
Abstract
Neural networks (NNs), with their powerful nonlinear mapping and end-to-end
capabilities, are widely applied in mechanical intelligent fault diagnosis
(IFD). However, as typical black-box models, they pose challenges in
understanding their decision basis and logic, limiting their deployment in
high-reliability scenarios. Hence, various methods have been proposed to
enhance the interpretability of IFD. Among these, post-hoc approaches can
provide explanations without changing model architecture, preserving its
flexibility and scalability. However, existing post-hoc methods often suffer
from limitations in explanation forms. They either require preprocessing that
disrupts the end-to-end nature or overlook fault mechanisms, leading to
suboptimal explanations. To address these issues, we derived the
cyclic-spectral (CS) transform and proposed the CS-SHAP by extending Shapley
additive explanations (SHAP) to the CS domain. CS-SHAP can evaluate
contributions from both carrier and modulation frequencies, aligning more
closely with fault mechanisms and delivering clearer and more accurate
explanations. Three datasets are utilized to validate the superior
interpretability of CS-SHAP, ensuring its correctness, reproducibility, and
practical performance. With open-source code and outstanding interpretability,
CS-SHAP has the potential to be widely adopted and become the post-hoc
interpretability benchmark in IFD, even in other classification tasks. The code
is available on https://github.com/ChenQian0618/CS-SHAP.
