Attention-Embedded Quadratic Network (Qttention) for Effective and Interpretable Bearing Fault Diagnosis

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 major challenges such as effectiveness and interpretability: 1) when bearing signals are highly corrupted by noise, the performance of deep learning models drops dramatically and 2) 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 these issues, 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 analog to the attention, making the model made of quadratic neurons inherently interpretable. Experiments on the public and our datasets demonstrate that the proposed network can facilitate effective and interpretable bearing fault diagnosis. Our code is available at https://github.com/asdvfghg/QCNN_for_bearing_diagnosis.

Automated summary

Bearing fault diagnosis is crucial for reducing damage risk and improving economic profits of rotating machines. Machine learning, particularly deep learning, has advanced in this field, but faces challenges in effectiveness and interpretability. To address this, a convolutional network with quadratic neurons is proposed to handle noisy bearing data, and an attention mechanism called "qttention" is derived to improve interpretability. Experiments show that the proposed network facilitates effective and interpretable bearing fault diagnosis. The code is available at https://github.com/asdvfghg/QCNN_for_bearing_diagnosis.