Rolling bearings are key components of aero-engine rotor systems, and their degradation can trigger abnormal dynamics and safety risks. Unplanned bearing failures increase downtime and inspection cost; early detection enables condition-based maintenance and reduces conservative replacement. Early faults are weak, intermittent, and often masked by operating variability, so diagnosis based on a single sensing modality is usually incomplete. Vibration signals describe global dynamic behavior and resonance responses, whereas acoustic emission (AE) captures micro-damage processes and short impulsive events. Given the differences in bandwidth and noise sources, combining vibration and AE can provide a fuller description of bearing states. This study proposes an STFT–MFDCNN framework for bearing fault diagnosis using fused vibration–AE information.
First, synchronized vibration and AE signals are converted into time– frequency images via the short-time Fourier transform (STFT), preserving spectral content and temporal non-stationarity in a unified representation. Second, a dual-branch multi-scale feature dense convolutional neural network (MFDCNN) is constructed. In each branch, parallel convolutions with different kernel sizes capture fault patterns across receptive fields, and dense concatenation connects all preceding feature maps to improve feature reuse and gradient flow. Third, global average pooling generates compact feature vectors for both branches, which are concatenated in a fusion head and classified by a Softmax layer. Experiments on a vibration–AE dataset built in this work show that the proposed method reaches 97.78% identification accuracy. Compared with dual-branch fusion baselines based on AlexNet, ResNet, and a shallow CNN, accuracy increases by 3.34%, 4.45%, and 5.00%, respectively. Compared with single- modality models (Only-AE and Only-Vib), accuracy improves by 14.71% and 6.77%.