Closing the Loop: Post Hoc Explanation Feedback-Augmented Deep Learning for Robust Machinery Fault Diagnosis

The “black-box” nature of deep diagnostic models fundamentally undermines trust in their deployment for high-stakes scenarios. While post-hoc explanation methods are mainstream techniques for revealing decision-making logic, they primarily produce visual interpretations (e.g., heatmaps) without yielding tangible performance improvements in the models themselves. This paper proposes a post-hoc explanation augmented deep learning (PHExp-DL) framework that closes the loop between interpretability and performance enhancement in fault diagnosis. Capitalizing on the intrinsic cyclostationarity of rotating machinery vibrations, we introduce a cyclic orthogonal convolution (CyclicOrthoConv), a novel convolution operator that could embed fault-induced cyclostationarity priors. Crucially, a dynamic gradient feedback scheme propagates gradient-weighted class activation mapping (Grad-CAM)-derived feature contributions backward, steering convolutional neural network (CNN) feature learning toward discriminative fault signatures. This creates a self-correcting learning cycle where explanation-guided feedback continuously refines feature extraction. Experimental results confirm the remarkable robustness of the proposed method under unseen noise conditions. It outperforms the baseline CNN by margins of 21.66% and 18.86% in average accuracy for the two Cases, respectively, and also exceeds the performance of other state-of-the-art methods.