Transparent Operator Network: A Fully Interpretable Network Incorporating Learnable Wavelet Operator for Intelligent Fault Diagnosis

The advent of Industry 4.0 has heightened the demand for the interpretability of intelligent diagnostics, especially for high-risk industrial assets. However, the comprehensive interpretation of neural networks remains inadequately explored. To address this challenge and develop a fully interpretable network, we propose a transparent operator network incorporating a parameterized signal operator node. This node is realized by a learnable Morlet wavelet operator in frequency domain with signal-wise gated matrix and skip connection. By stacking multichannel and multilayered nodes as signal operator layers, along with incorporating statistical features and a linear classifier, all modules are physically understandable. A case study demonstrates that despite having fewer parameters, the proposed model achieves better diagnosis performance. Furthermore, the learnable filters, fault signature enhancement, and physically understandable features demonstrate the transparency of the proposed model, indicating that it offers a promising tool for constructing a knowledge-informed and fully interpretable industrial decisions.