## 1 Introduction  
The roller system of annealing kilns is a crucial component of the glass production line for carrying and hauling glass products. The diagram of the annealing kiln roller system is shown in Figure 1. The bearings of the annealing kiln roller system include two types: roller and through shaft bearings. Roller and through shaft bearings are SKF-1218K double-row self-aligning ball bearings, which are seated in bearing seats to ensure the stability and smooth operation of the annealing kiln roller system. The roller bearings in a production line are used for loading and pulling glass products at the speed of 16 r/min, and the through shaft bearing is used to transmit power at the speed of 12 r/min. As the core components of the roller system, the stable operation and health status of roller and through shaft bearings significantly affect the quality of glass products [1]. Therefore, the timely health condition monitoring and abnormal diagnostic of the roller table and through shaft bearings are essential for intelligently maintaining the operational stability and safety for the quality and production efficiency of the glass product line. However, the condition monitoring signal of the roller bearing system is affected by the low rotating frequency and strong mechanical background noise, which shows the width impact interval and non-stationary multi-component characteristics. Moreover, the difference in the rotating speed, load, and working condition between the roller bearing and through shaft bearing obviously increases the difficulty of the real-time intelligent fault diagnosis and condition monitoring of the annealing kiln roller system. And the professional knowledge and prior knowledge of the working condition of the roller bearing system also limit the efficiency of identifying abnormal states. Thus, exploring new vibration signal preprocessing and intelligent transfer diagnostic methods is essential for health monitoring and fault detecting for the annealing kiln roller system. Affected by the working environment, the condition monitoring vibration signal of the bearing the annealing kiln roller system inevitably contains multiple non-stationary components and mechanical background noise. Hence, it is a prerequisite for improving the accuracy of fault diagnostics and the effectiveness of health monitoring to effectively decompose and filter out the noise component in the bearing vibration signal. Focused on denoising and filtering methods based on signal decomposition, classical fault diagnosis methods, such as ensemble empirical mode decomposition [2; 3], local mean decomposition [4], and variational mode decomposition [5] methods, were wildly used to filter out background noise from nonlinear and non-stationary vibration signals. However, these methods cannot adaptively decompose the non-stationary vibration signal for separating meaningful  
Figure 1: Diagram of the structure of the roller table system of the annealing kiln. information based on the time-scale, and endpoint effects, modal aliasing, and noise interference inevitably affect the decomposition results [6; 7; 8]. In order to effectively avoid these drawbacks, a novel method named intrinsic time scale decomposition (ITD) was introduced to decompose the vibration signal into several amplitude and frequency demodulation proper rotation single components [9; 10]. On the basis of linear transform, the ITD method can decompose the signal into several proper rotation components, which are mono-components in nature and suitable for calculating the instantaneous frequency and amplitude. Moreover, ITD defines the instantaneous amplitude and frequency of signals on the basis of single wave analysis, thus overcoming the limitations of modal aliasing and noise interference and providing an approach to the decomposition of vibration signals into several mono-components for further demodulation analysis.