The fusion of multiple monitoring sensors is crucial to improve the
accuracy and robustness of machinery fault diagnosis. However, existing
fault diagnosis methods may underestimate the interference of noise in
the multi-sensor fusion process, leading to unsatisfied performance. To
handle this problem, this paper proposes a deep model based on the
frequency adaptive wavelet pyramid. First, an adaptive frequency
selection strategy is designed to prune the seriously polluted
frequencies and only retain some key frequencies. Then, the
self-attention mechanism is used to perform information fusion on the
selected frequency bands of different sensors. Finally, a wavelet fusion
pyramid is adopted by repeating the fusion process at multiple wavelet
decomposition levels. In this way, different sensors can be fused in a
more fine-grained manner. The experimental results on two
multi-sensor-based fault diagnosis datasets demonstrate the anti-noise
capability of our proposed method.