## 4 Results and Discussion
### Data Processing  
In the actual experiments, the vibration signal of the transformer core during operation was simultaneously affected by other components and external environment interference. \begin{table}
\begin{tabular}{c c} \hline Name & Type \\ \hline N1 & Normal \\ F1 & Loose yoke on phase A \\ F2 & Loose yoke on phase B \\ F3 & Loose yoke on phase C \\ F4 & Loose lower yoke of phase A \\ F5 & Loose lower yoke of phase B \\ F6 & Loose lower yoke of phase C \\ \hline \end{tabular}
\end{table} 
Table 1: Typical types of faults. Figure 5: The transformer vibration test platform. These interferences can cause the GAF to generate blurry images, which is not conducive to subsequent image recognition. Therefore, this article adopts EEMD to eliminate interference signals. EEMD utilizes the characteristic that Gaussian white noise energy is equal across all frequencies. EMD is a completely data-driven, adaptive decomposition method that decomposes a signal from high to low frequency into a finite sum of physically significant Intrinsic Mode Functions (IMFs) and remainders. By adding white noise to the initial signal and performing multiple EMD processes, a series of linear and stationary IMF components are obtained, which decompose the initial signal into the sum of several single-component amplitude and frequency modulation signals. This process can be regarded as linear filtering. The main steps are as follows:  
1. Add Gaussian white noise with a mean of 0 to the decomposed signal \(x_{i}(t)\) and normalize it. Apply the EMD algorithm to decompose the normalized signal and obtain the IMFs of each order. 2. Repeat step (1) and ensure that the intensity of Gaussian white noise added each time is the same, but the sequence is different. 3. Perform ensemble averaging on the IMF obtained in step (2) to obtain the EEMD of the initial signal, as shown in the following equation. \[x(t)=R(t)+\sum_{s=1}^{S}IMFs\] (13) where \(R(t)\) is the residual component and \(S\) is the number of IMF components. EEMD decomposes the original signal by adding white noise, so there may be false components in the numerous IMF components obtained from the decomposition. Therefore, by calculating the correlation coefficient \(r\) between each IMF component and the original signal, IMF components that are strongly similar to the original signal (\(r>0.6\)) can be selected and considered as a true component of the original signal. Otherwise, they can be discarded. The calculation steps for the correlation coefficient are as follows [25]:  
1. Calculate the autocorrelation functions of each IMF component, \(C_{IMF1}\), \(C_{IMF2}\)..., \(C_{IMFS}\), and the autocorrelation function of the original signal, \(C_{x}\), using the following formula: \[C_{x}(m)=\frac{1}{N}\sum_{i=0}^{N-1}x(i)x(i+m)\] (14) where \(x(i)\) is the state of the signal at a certain moment and \(N\) is the number of sampling points of the signal. 2. Normalize the autocorrelation coefficients and calculate the correlation coefficients \(r(j)\) between \(C_{IMF1}\), \(C_{IMF2}\)..., \(C_{IMFS}\), and \(C_{x}\) using the following formula: \[r(j)=\frac{\sum\limits_{i=1}^{2N-1}R_{IMFj}(i)R_{x}(i)}{\sqrt{\sum\limits_{i=1 }^{2N-1}R_{IMFj}^{2}(i)\sum\limits_{i=1}^{2N-1}R_{x}^{2}(i)}}\] (15) where \(r(j)\) represents the correlation coefficient between the jth IMF component and the original signal. It is generally believed that when \(r(j)>0.6\), the corresponding IMF component has good correlation with the original signal and can be retained. Select these IMF components with good correlation to the sum and reconstruct the signal. Taking the vibration signal of the B-phase lower-yoke bolt loosening as an example, the vibration signal of the transformer iron core is decomposed by EEMD. The correlation coefficients between each IMF component and the original signal are shown in Table 2.  
After EEMD, eight IMF components were obtained. Based on Table 2, IMF1 was chosen to reconstruct the signal.