Fault Diagnosis Study of MCSECNN Model Integrating MK-MMD and DANN under Variable Operating Conditions

Bearing signals measured under different loads rarely keep the same waveform characteristics. Their amplitudes, spectral components, and statistical properties may change at the same time, which makes a classifier trained on one load condition less reliable on another. This study investigates a cross-load diagnosis method that trains a multi-scale convolutional squeeze-excitation network (MCSECNN) with multi-kernel maximum mean discrepancy (MK-MMD) and a domain-adversarial neural network (DANN). The convolutional branch captures vibration patterns over several temporal ranges, and the SE unit adjusts the importance of the channels generated by the parallel kernels. MK-MMD penalizes the distance between load-dependent feature distributions, whereas DANN weakens the load identity retained in the features. Tests using CWRU bearing data show that the integrated model gives better accuracy than the basic MCSECNN and the two single-adaptation variants. Ablation results indicate that the statistical and adversarial constraints contribute to transfer performance in different ways.