## 3 Results and Discussion  
The TimeGAN network used for augmentation of healthy data consists of three main components: an encoder, a generator (decoder), and a discriminator. Each of these modules has its own layer structure, adapted to its function. The encoder, composed of three layers, transforms the input time sequences into a representation in the latent space, using an input layer to receive data, an LSTM layer with 128 units to model temporal dependencies, and a fully connected layer with a dimension of 24 to encode information in the form of a latent vector. The generator (decoder) has a similar structure and consists of an input layer, an LSTM layer with 128 units, and a fully connected layer that transforms the encoded data into synthetic time sequences with the same number of features as the real data. The discriminator, on the other hand, is responsible for evaluating the authenticity of the generated samples and consists of four layers: an input layer, an LSTM layer with 128 units, a fully connected layer, and a sigmoid activation layer, which returns the probability that a given sample comes from the real dataset. The Recovery layer is not explicitly defined as a separate component. In this implementation, the Recovery function is taken over by the generator, which reconstructs the time data based on the latent vector. This configuration  
Figure 12: Indications of strain gauges T1,T2,T3 for the conveyor belt without load at a speed of 540 rpm with three longitudinal belt damages: DL1, DL2, DL3 and one transverse damage DT1. is possible because TimeGAN combines the features of a GAN and an autoencoder. In this role, the generator not only creates synthetic data but also reconstructs real sequences, which is sufficient for learning temporal representations and their realistic reconstruction. In total, the entire TimeGAN network consists of 10 layers and uses an architecture based on recurrent neural networks, which enables effective modeling of complex temporal dependencies in data and generating realistic sequences. For samples with damages, the system responses to local longitudinal and transverse tape damages were separated. Then, using Equation (1) and changing the gain \(k_{i}\) and offset, the system's responses to damages of various sizes and locations were obtained. In this way, a set of runs with tape damages of various sizes and locations and in various configurations was obtained. The architecture of the LSTM network consists of 14 layers. It includes an input layer with dimensions of 1 xData Sequence Length x 3 channels (corresponding to three strain gauges). The network contains two downsampling blocks, each including a 1D convolution, ReLU activation, and dropout layers. In addition, there are two upsampling blocks that use 1D transposed convolution, ReLU activation, and dropout layers. To ensure that the output sequences retain the same number of channels as the input, a 1D transposed convolution layer should be included, with the number of filters corresponding to the number of input channels. The modification of the algorithm consisted of adding an attention mechanism to the LSTM network. The attention mechanism was implemented by adding two fully connected layers that calculate the so-called attention score for each time step. These scores are then normalized by a SoftMax function, which allows assigning appropriate weights to the hidden states of the LSTM. The resulting context vector, which is a combination of states weighted by the attention mechanism, replaces the traditional approach of using only the last LSTM state. Additionally, another LSTM layer was added after the attention layer to allow further processing of the already optimized signal. Transposed convolutional layers were used in the decoder to reconstruct the signal waveform based on the obtained context vector. Thanks to these modifications, the model more effectively reconstructs time signals and better detects important patterns, improving interpretation and accuracy of anomaly detection.