## 1 Introduction
Hence, considering the strong noise interference and low speed and width impact interval characteristics in the bearing vibration signal of the annealing kiln roller system, the ITD method is introduced to decompose the signal into several components with different frequencies, and the composed index with kurtosis [11] and the Gini index [12] are used to select the demodulation frequency component with the optimal pulse and sparse characteristics of fault information. The selected components are used to reconstruct the denoised signal for further identification. To improve the time-frequency representation ability of bearings under low-speed operating condition information, which have strong nonlinearity and non-stationary characteristics under complex working conditions, continuous wavelet transform (CWT) [13] is used to extract the time-frequency representation with different scales of denoised signals, and histogram equalization is used for processing the time-frequency image through non-linear straightening, reassigning image pixel values, and improving the image contrast. Based on the denoised signal, reducing the reliance on professional experience and prior knowledge for bearing abnormal detection and intelligent diagnosis have significant advantages for improving the performance of intelligently maintaining and the condition monitoring of the glass production line. Deep learning methods have been widely utilized in the field of mechanical fault diagnosis [14]. Focused on the multi-scale and dependency characteristics, [PERSON] [15] combined Inception with an attention mechanism to extract the multi-scale features of bearing faults and found that the recognition effect using the Inception module is significantly better than that using a single CNN. [PERSON] [16] used CNN and long short-term memory (LSTM) to extract features under variable load and noise conditions and found that the recognition accuracy of the proposed method is higher than that using CNN or LSTM alone. [PERSON] [17] proposed a novel deep neural network based on bidirectional-convolutional LSTM to determine the type, location, and direction of planetary gearbox faults by automatically and simultaneously extracting spatial and temporal features from both vibration and rotational speed measurements. However, these methods focused on the static data and their features and not so much on time-varying non-stationary data. The complex deep features in the time dependency, high-dimensional, and noisy real-world vibration signal cannot be adaptively learned with the shallow model, which constructed only several numbers of non-linear operations, and it could effectively model such complex data. Therefore, considering the multi-scale feature-extraction ability of Inception and the time-state reliance representation ability of LSTM, an variable scale attention mechanism-guided time-frequency feature fusion was constructed to further extract deep differential and abnormal features and improve the effectiveness of the bearing fault diagnosis. As the crucial transmission part, the health condition of the through shaft bearings can also significantly affect the stability of the glass production line. But, affected by the rotating speed and working environment, the failure probability and vibration signal characteristics are apparently different from the roller table bearings. Therefore, when diagnosing roller bearing faults, how to accurately diagnose the through shaft bearing faults simultaneously is the key to effectively monitoring the health condition of the roller table system of the annealing kiln. As an excellent novel deep learning method, transfer learning aims to apply existing knowledge in evaluating and recognizing similar featuresbetween source and target domain data with different distribution characteristics [18; 19]. As a nonparametric distance metric, MMD maps data to a reproducing Hilbert kernel space to evaluate the distribution characteristics and distance; therefore, the selection of kernel functions is crucial [20; 21].