Attention-embedded Quadratic Network (Qttention) for Effective and Interpretable Bearing Fault Diagnosis
This work addresses the interpretability issue in bearing fault diagnosis for industrial maintenance, but it is incremental as it builds on existing quadratic neuron methods.
The paper tackled the interpretability problem in deep learning for bearing fault diagnosis by introducing a quadratic neuron-based network with an attention mechanism derived from quadratic functions, achieving effective and interpretable diagnosis on public and custom datasets.
Bearing fault diagnosis is of great importance to decrease the damage risk of rotating machines and further improve economic profits. Recently, machine learning, represented by deep learning, has made great progress in bearing fault diagnosis. However, applying deep learning to such a task still faces a major problem. A deep network is notoriously a black box. It is difficult to know how a model classifies faulty signals from the normal and the physics principle behind the classification. To solve the interpretability issue, first, we prototype a convolutional network with recently-invented quadratic neurons. This quadratic neuron empowered network can qualify the noisy bearing data due to the strong feature representation ability of quadratic neurons. Moreover, we independently derive the attention mechanism from a quadratic neuron, referred to as qttention, by factorizing the learned quadratic function in analogue to the attention, making the model with quadratic neurons inherently interpretable. Experiments on the public and our datasets demonstrate that the proposed network can facilitate effective and interpretable bearing fault diagnosis.