## 3 Gas Turbine Rotor System Fault Diagnosis Method Based on Improved DCGAN  
### The Improved DCGAN  
In traditional GAN, random noises are generally used as signal sources to enter into the generator \(G\), and new signals and samples are generated after processing by the network. In order to make the generated signal consistent with the real signal, a discriminator \(D\) is trained and used to identify whether the signal is real data or fake data generated by the generator. The random noise generated each time during the operation is a special code that only a group of generators can understand, and it determines how the generated result should be represented. The result generated by using random data as input barely has a relationship with the original signals, leading to low similarity between the signals generated by the generator and original sample signals and long training time of the network.  
Each convolutional layer in a Convolutional Neural Network consists of several convolutional units, and the parameters of each convolutional unit are optimized through the back-propagation algorithm. The purpose of the convolution operation is to extract different features of the input. The first convolution layer may only extract some low-level features such as edges, lines, and corners. The network with more layers can iteratively extract more complex features from the low-level features. The transposed convolutional layer can synthesize pictures or signals based on features, and has been used to generate adversarial networks such as DCGAN.  
If the advantages of the convolutional layer and the transposed convolutional layer in CNN can be combined, a new generator can be formed. Therefore, in order to obtain the generated samples with higher similarity to the original samples, an Improved DCGAN method is proposed. Figure 3 is shown as the structure of Improved DCGAN. On the basis of DCGAN, the trained WDCNN network is introduced to the generator. The features of input fault signals are obtained through WDCNN network training, so the input contains fault features and the generated samples similar to the original samples are easier to obtain through the generator.  
Figure 3: Structure of Improved DCGAN.  
The generator of the Improved DCGAN network consists of three parts: an encoder network, the reshape network, and a decoder network. The encoder network extracts the feature representation of the input signals, and WDCNNs are used as convolutional layers to construct the encoder. The reshape network consists of the project and reshape layer, which upscales the input using a fully connected operation and reshapes the output to the specified size. The decoder network consists of transposed convolutional layers, and takes the transformed features and outputs the final generated signals as samples.  
The discriminator examines patches of the input samples and determines the probability of the samples being real or fake. As can be observed, the discriminator has an input layer of 32 \(\times\) 1. Three hidden layers are employed with LeakReLU as the activation function. Finally, the output layer has a dimension of 256 \(\times\) 1, which is fully connected with a sigmoid activation function for the real and fake data classification. The kernel size for the CNN is 3 \(\times\) 1 in all its layers with stride of 2 for all the hidden layers except by the output with stride of 1. The structure parameters and hyper-parameters of Improved DCGAN are shown in Table 2.