## 1 Introduction
And this method not only performs well on data with few or insufficient samples but also has good identification performance based on cross-domain distribution difference data of the source and object domain. Therefore, based on the feature fusion network, considering the similarities and differences between annealing kiln roller and through shaft bearings, the transfer learning method is adopted to achieve domain invariant feature extraction and cross domain diagnosis, with the Multi Kernel Maximum Mean Difference (MK-MMD) as a distance indicator, roller bearing vibration data as the source domain, and through shaft bearing vibration data as the target domain, and an intelligent transfer diagnosis model for through shaft bearing faults is constructed. To monitor and diagnose the real-time health condition of the bearing in the annealing kiln roller system in glass production lines, this paper proposes a time-scale data-driven vibration signal adaptive denoising and intelligent transfer diagnosis method based on a variable-scale attention mechanism-guided time-frequency feature fusion transfer learning method for the abnormal state detection and fault diagnosis of roller and through shaft bearings in the annealing kiln roller system. First, the strong mechanical background noise in the vibration signal is filtered using ITD and the Gini-kurtosis criteria, and the time-frequency representation of the denoised signal is obtained using CWT, and the time-frequency images are enhanced through histogram equalization. Then, according to Inception and LSTM, an intelligent diagnosis network based on a variable-scale attention mechanism-guided feature fusion framework is constructed to extract multi-scale features from the time-frequency representations and accurately identify the fault information of roller bearings. Furthermore, based on the intelligent diagnosis network, an intelligent transfer diagnosis model for through shaft bearings is constructed using MK-MMD. The vibration signals of the roller and through shaft bearings are used as the source and target domains, respectively, to accurately diagnose the operating condition of through shaft bearings. Finally, two kinds of vibration signal data are conducted to verify the proposed method for bearing faults in the annealing kiln roller bearing system. The results show that the proposed method can efficiently extract the bearing fault features and accurately identify the health condition of the bearings in the annealing kiln roller bearing system. The rest of this paper is organized as follows. Section 2 reviews the concept of the Inception module, LSTM network, and MK-MMD method briefly. In Section 3, the proposed variable-scale attention mechanism-guided feature fusion module is introduced firstly in detail; then, the neural network framework and procedure of intelligent fault diagnosis and transfer diagnosis of the annealing kiln roller bearing system are descripted comprehensively. After that, the experiment data from the test rig are used to comprehensively verify the effectiveness of this proposed neural network framework and fault diagnosis method in Section 4. And the proposed method is introduced to diagnose measured data from the plant of the annealing kiln roller bearing system. The discussion and conclusion are presented in Section 5.