*4.2. Case Two*

### 4.2.1. Dataset Preparation and Parameter Settings

The AF-CDN is also verified on the CWRU Bearing Dataset [33]. CWRU set up a variety of fault types on motor drive equipment. The vibration signals of the different positions of the drive end were collected by vibration sensors under different load conditions.

According to the different fault locations, the fault can be divided into three categories: the ball fault (BF), inner ring fault (IF), and outer ring fault (OF). Each type of fault can be further subdivided into three different fault levels according to different fault severity. Therefore, a total of nine types of faults were set up in this experiment, and the sampling method was carried out according to the paper [6,35]. For details about fault information, see Table 3. The size of each sample is 400, so the dimensions after the 2D conversion are 20 × 20. In order to ensure the speed of iteration, the batch size is set to between 20 and 40.

The continuous time signal collected by the vibration sensor is shown in Figure 11.


**Table 3.** Status information of 9 types of fault.

**Figure 11.** Continuous time signal diagram of 9 kinds of faults.

4.2.2. Experiment and Analysis

The AF-CDN is compared with some popular methods at present. As summarized in the previous chapter, the existing methods are mainly based on two signal extraction methods. One involves performing frequency domain transformation in the original timecontinuous signal to obtain frequency domain features. The other mainstream approach is based on raw signals.

In this case, the existing methods and AF-CDN are compared in detail. The processing methods in the frequency domain mainly include wavelet transform, wavelet packet transform, statistical locally linear embedding, and other methods. Raw signal processing involves converting raw signals into 2D images and establishing Spectrogram methods.

Table 4 shows the comparison of experimental results between the existing mainstream methods and AF-CDN.


**Table 4.** Comparison of diagnostic accuracy of different methods.

Compared with the traditional diagnosis methods, the proposed algorithm effectively integrates the frequency domain characteristics of vibration signals and their original signal characteristics. The accuracy of network diagnosis is further improved.

The main feature extraction methods used in the signal-based feature analysis approach include WP, WT, DWT, and statistical methods based on these. After obtaining these frequency features, KNN, SVM, ANN, etc., can be used for analysis. As shown in Table 4, such diagnosis methods based on "frequency domain signals + neural networks" have a diagnostic accuracy of 62.5–98.7%. The accuracy of the analysis method based on the raw signal is 98.1% to 99.5%. The diagnostic accuracy of the proposed method is 99.44–99.78%. The average execution time of AF-CDN is about 0.115 s. Compared with the traditional diagnosis methods, AF-CDN effectively integrates the frequency domain characteristics of vibration signals and their raw signal characteristics. The feature extraction capability of the network is excellent compared to the rest of the network structure. As analyzed in Case One, AF-CDN combines information on the frequency domain characteristics of the signal with the raw time domain information. Thus AF-CDN is able to have an excellent diagnostic result. The accuracy of network diagnosis is further improved.
