Interpretable Fault Diagnosis

We propose a Sparse Temporal Logic Network for interpretable bearing fault diagnosis. The framework combines wavelet-based predicate extraction, sparse and structured neural attention, and temporal logic inference to deliver accurate diagnosis together with formal, human-readable explanations.

We develop a neural-symbolic learning architecture for interpretable rolling-bearing diagnosis that combines weighted signal temporal logic, predicate extraction, autoencoding, and timed failure propagation graphs to produce accurate and explainable fault decisions.

We introduce shapelet temporal logic, a formal language that describes temporal relations among discriminative shapelets in sequential data. An incremental inference algorithm with theoretical guarantees is developed to obtain interpretable fault diagnosis rules for rolling element bearing signals.

In recent years, causal learning has provided application prospects for revealing the internal causal relationships of equipment and the explainability of intelligent diagnostic models. However, existing methods still have limitations of the difficulty in eliminating spurious causal correlations in high-dimensional data and insufficient explainability, leading to unstable and unreliable diagnostic performance in unseen domains. Aiming at the above problems, an interpretable fault diagnosis method based on causal invariant graph neural network (CIGNN) is proposed to enhance model’s accuracy and interpretability for gears in the unseen domain. Firstly, a structural causal model is constructed from the cross-domain perspective and combined with GNN to clarify the internal causal mechanism of faults. Then, a causal disentanglement refining module is proposed to separate the effective causal parts from the high