**3. The Proposed Method**

*3.1. Adaptive Fusion Convolutional Denoising Network*

The proposed AF-CDN structure diagram is shown in Figure 2. Firstly, the equipment data are collected by vibration sensors. Then, the 1D data are obtained by FFT and fed into the 1D channel of the perception layer. The raw data are arranged in order to obtain 2D data. These data are then fed into the 2D channel of the perception layer. After feature extraction in the perception layer, all feature values are flattened and then pooled. The features then go through two more inception and pooling layers. The output values are fed to the auxiliary classifier for classification after each pooling layer. The final pooling layer output is combined with the output of the auxiliary classifier to provide the final diagnosis result. The loss value is calculated from the output, and if the predetermined loss condition is satisfied, then the training is stopped; if not, then training continues.

**Figure 2.** AF-CDN structure diagram.

Numerous research results in the field of CV have favorably illustrated the importance of 2D convolution for the accurate recognition of valuable information in 2D images. As shown in Section 2.2, the development of convolutional neural networks in the field of fault diagnosis also started with 1D-CNNs, and then researchers successively proposed various methods to convert 1D data into 2D data, thus enabling 2D-CNNs to be widely used in the field of fault diagnosis. The schematic diagram of the data processing is shown in Figure 3. After the original data are FFT-transformed, the positive half-axis frequency data are taken and arranged sequentially to obtain 1D data. Two-dimensional data, on the other hand, are arranged with the raw data starting from the first row of the matrix, followed by the second row, until the entire matrix is filled. During the 2D conversion, the signals are arranged sequentially, so the time-series property of the signals is preserved.

**Figure 3.** One-dimensional and two-dimensional data transform.

The algorithm flow chart of AF-CDN is shown in Figure 4. The main steps of the proposed algorithm are described as Algorithm 1.

**Figure 4.** Algorithm flow chart of AF-CDN.
