## 3 Proposed Method
### Image Fusion  
Images can intuitively reflect relevant characteristics of signals, such as time-frequency features, periodic features, autocorrelation features, and state transition features. In complex mechanical structures, the operating state of bearings is coupled with the states of other components, and the vibration signals are significantly affected by noise and random shocks. In such cases, using a single image processing method to characterize the signals from one perspective may fail to capture the complete information of the bearing vibration signals. This can hinder the model's subsequent fault diagnosis and identification of the bearings.  
This study processes signals using a image fusion method. Specifically, it fuses the MTF, RP, and CWT images through the R, G, and B channels, respectively. This approach enables the two-dimensional images to represent the original signal from multiple perspectives, offering a more comprehensive and enriched depiction of the signal's characteristics. Figure 3 illustrates the complete image construction process.  
**Step 1:** Use a sliding window to segment the raw vibration signal. The sampling length of the image is determined by the frequency at which the vibration sensor collects the signal and the rotational speed of the motor. The sampling points under different working conditions in the same dataset should be kept consistent. To ensure that a single image fully describes the fault, the sampling length should at least cover one complete rotation of the bearing. When the rotational speeds under different working conditions in the same dataset are different, the sampling length should be at least greater than the maximum sampling length among all conditions. Detailed information can be found in the data description section of the two datasets selected in this paper. The specific equation for calculating the sampling length is as follows:  
\[N=\frac{f_{z}}{n/60} \tag{11}\]  
where \(f_{z}\) is the sampling frequency and \(n\) is the rotational speed.  
**Step 2:** To ensure a smooth transition at the window boundaries and to retain the complete detailed characteristics of the signal, this study uses a 50% overlap in the data when sliding the window.  
Figure 3: MCR image fusion process.  
**Step 3:** Each segmented data sample is processed using MTF, CWT, and RP image processing methods. The resulting color images are then converted to grayscale images to serve as the three inputs for the image fusion method.  
**Step 4:** The generated grayscale images from MTF, CWT, and RP are used as inputs for the R, G, and B channels, respectively, for image fusion. To preserve the important features of the images, each channel is multiplied by a coefficient. In this study, the coefficients for the R, G, and B channels are set to [0.2, 1, 0.2]. The final result is the processed fused image.  
The fused images obtained using the above processing steps simultaneously display the time-frequency characteristics, periodic features, autocorrelation properties, and state transition characteristics of the original signal. This provides a rich data foundation for subsequent models to extract fault features.