A Noise-Resilient Gearbox Fault Diagnosis Method Using Gaborlet Atlas-Guided Autoencoder Network

Accurate and efficient feature extraction remains a critical challenge in gearbox fault diagnosis, particularly under noisy operating conditions. To address this issue, a novel Gaborlet atlas-guided autoencoder network (GAAN) that leverages the unique feature extraction capabilities of the Gabor transform is proposed in this article. Despite its inherent inability to form an orthogonal basis, it possesses the capability to construct a compact frame under specific parametric conditions, thereby facilitating the utilization of the Gaborlet atlas in feature extraction tasks. Multiheaded attention mechanism is also used to assist training the weight matrix of the autoencoder network. The GAAN can capture multiscale and multidirectional fault features from noise signals, enhancing both interpretability and noise resilience of the diagnostic model. Extensive experiments on two noisy gearbox datasets demonstrate that GAAN outperforms traditional methods in both accuracy and computational efficiency, highlighting its potential for real-world industrial applications.