## 3 Method
### Parallel Attention
#### 3.2.2 Proposed Parallel Attention  
The working environment of electrical equipment usually contains various noises, such as mechanical noise, electromagnetic interference, etc. These noises may mask the abnormal audio signals of the equipment. Generally, the audio features of electrical equipment usually contain high-frequency information, such as partial discharge, and low-frequency information, such as mechanical vibration. Previous work lacks effective use of the phase features of audio  
average pooling operation to reduce spatial resolution, thereby preserving low-frequency information and entering the low-frequency attention pathway. In high-frequency attention, the input features are processed through a phase-aware multi-head self-attention mechanism. This phase-aware multi-head self-attention mechanism is similar to the standard multi-head self-attention mechanism but adjusts the attention weights based on the phase information of the input features. The processing in the low-frequency attention pathway is similar to that in the high-frequency attention pathway, with the difference being that the input features have reduced dimensions after average pooling, resulting in lower computational overhead. Similarly, low-frequency information is processed through the phase-aware multi-head self-attention mechanism to obtain the output of the low-frequency attention pathway. Finally, the outputs of the high-frequency and low-frequency attention pathways are concatenated to form a feature tensor. This concatenated feature is then further fused and transformed through a fully connected layer or convolutional layer to produce the final output feature. The design of this parallel attention module effectively captures information at different frequencies and enhances sensitivity to phase information through the phase-aware mechanism, thereby improving the model's performance. Specifically, the advantage of the parallel attention module lies in its ability to simultaneously process information at different frequencies, capturing details and high-frequency features through the high-frequency pathway and capturing overall structure and semantic information through the low-frequency pathway. This parallel processing approach not only increases the efficiency of the model but also enhances its ability to express features at different frequencies. Moreover, the phase-aware mechanism allows the model to better utilize the phase information of the input features, which is crucial for audio classification tasks, as phase information often contains rich semantic and structural information. The proposed phase-aware multi-head self-attention module in Figure 2 is an innovative attention mechanism designed to enhance the model's ability to understand and process input features by incorporating phase information, inspired by [32]. The structure of this module is depicted in Figure 3.  
First, the input features are fed into two parallel branches, each containing a phase extraction module to extract the phase information of the input features. Given an input \(X_{in}\), this operation is  
\[M_{X}\angle\varphi_{X}=FFT2d(X_{in}), \tag{6}\]  
\[X_{out}=IFFT2d(|M|\angle\varphi_{X}), \tag{7}\]  
Figure 2: The architecture of proposed parallel attention block. where \(M_{X}\) and \(\angle\phi_{X}\) are amplitude and phase information of input, \(M\) is a stable value, and we set \(M=1\) in this article. Through phase extraction, the model can better capture the phase characteristics in the input features, thereby increasing sensitivity to different frequency information. Next, the phase information in each branch undergoes a \(1\times 1\) convolution operation to linearly transform and reduce the dimensionality of the phase features.