智能信号处理

We develop an adaptive ridge path regrouping framework for instantaneous frequency extraction in overlapped multicomponent signals. The method combines a variation-constrained time-frequency filter, entropy-guided parameter adaptation, and a refined penalty term to improve robustness and accuracy in complex overlapping regions.

We formulate fault diagnoser construction as a formal language generation problem and introduce signal spectral logic for interpretable diagnosis in the frequency domain. Adversarial training is used to alleviate the sparse-reward issue and improve the robustness of generated logic formulas under noisy conditions.

We introduce a frequency-temporal logic (FTL) framework for bearing fault diagnosis and interpretation. The method combines Bayesian optimization with Bayesian neural networks to infer compact and interpretable logic formulas from vibration data, achieving competitive diagnostic performance while providing human-readable explanations of fault patterns.