# Acoustic Emission Based Fault Detection of Substation Power Transformer
## 2 Dataset  
The samples of AE signals were collected from 36 transformers located in six transformer stations. The length of each segment of AE signals is about 10 min. To ensure uniformity of all collected AE signals samples, the same sampling equipment is used for each acquisition, and the sampling rate is fixed. The acquisition equipment includes a pickup connected with the Intel NUC5i7 RYB PC fixed in an acoustic and vision inspection robot (Figure 1a). The original sampling rate is set at 22,050 Hz, and then the output signals were downsampled to 16 k/s and encoded to 16-bit format with a single-channel configuration. The measurements were made in real time and all data were obtained in natural scenarios. For analyzing these data, every 10 min of data is linear normalized as follows.  
\[\text{Y}=(2\times(\text{X}-\text{X}\_\text{min}))/(\text{X}\_\text{max}- \text{X}\_\text{min})-1 \tag{1}\]  
where X is the original AE signals data, X_min represents the minimum amplitude in the AE signals data, X_max represents the maximum amplitude in the AE signals data, and Y is the data after normalization.  
As the AE signals of the transformer is basically long time stationary, in the following processing step, the level-adjusted signal is subdivided into overlapping fragments of 1.0 s duration (16 K samples) with an overlap of 0.5 s, which are classed and labeled with normal clean, normal with bird call, normal with wind, normal with rain and normal with the human voice, discharge and mechanical sliding signals.  
Figure 1 also illustrates the spectrogram of the seven kinds of acoustic patterns. It can be seen from the spectrograms, apart from the normal signals with bird call and human voice, other patterns are time-stationary. Whether with noises or not, there are obvious visual differences between the normal signals Figure 1b-f and the fault signals Figure 1g,h. However, within the normal signals b, d and e, the difference is not notable, strictly to say, they are all the normal signal in practice and need not to be distinguished, in this paper, we make the discrimination for them just for test the performance of different classification methods. The difference between the two fault signals Figure 1g,h is bigger than the normal signals. The normalized energy in the spectrogram of discharge signals is more concentrated in the low frequency region, and in high frequency region, the transient energy concentration can be seen randomly.  
Figure 1: Acoustic and vision inspection robot (**a**) and spectrograms of seven different types of AE signals send by transformers recorded by the robot: they are (**b**) Clean normal signals sent by the transformer; (**c**) Normal signals with bird call; (**d**) Normal signals with wind sound; (**e**) Normal signals with rain sound; (**f**) Normal signals with voice; (**g**) Discharge signals of transformer; (**h**) Mechanical sliding signals sent by the transformer.  
Therefore, when we are extracting the feature of the signals, we don't think the sound of wind, rain, birds singing and human voice as noises and carry suppression and removal operation.  
After selection (deleting some blank and invalid data), the total number of the sound fragments of 1.0 s duration used in the following experiments is 15,520, and tenfold cross validation was used for testing the reliability of the classification system, in each test the training set includes 90% of total samples, and 10% of total samples used as the test samples. The specific description as shown in Table 1. We can see that the number of samples for different classes is seriously unbalanced which leads to over fitting of the solution and lack of generalization ability.