Acoustic–vibration fusion bearing fault diagnosis via a multi-scale Swin–CNN hybrid architecture
An acoustic–vibration fusion method for bearing fault diagnosis based on a multi-scale Swin–CNN hybrid architecture that employs a Bayesian optimization-based tunable Q-factor wavelet transform (BO-TQWT) to enhance fault-sensitive subbands under low signal-to-noise ratio conditions, and converts acoustic and vibration signals into two-dimensional time–frequency maps.