## 1 Introduction
While the strain gauge system does not directly measure belt thickness or offer the same precise tear localization as more advanced methods like ultrasonic or laser systems, it serves as an effective and cost-efficient early warning tool. It is an ideal choice when low cost and simple integration are priorities. However, when precise tear detection and wear prediction are critical, advanced systems remain the optimal choice. The research presented in this paper aims to develop and test a monitoring system that, through a unique combination of early warning capabilities and cost effectiveness, minimizes or eliminates the aforementioned limitations, offering a valuable solution for a wide range of applications. In real-world industrial conditions, the number of defective components is relatively low compared to properly functioning elements. Consequently, there is a limited number of labeled fault samples, which poses a challenge for optimizing generative deep learning models and feature extraction methods. These methods aim to reduce subjectivity in user-driven selection tasks and enhance training datasets [15, 16, 17, 18]. In the proposed solution, measurements were conducted using a specially designed strain gauge system mounted on one of the conveyor model's rollers [5,19]. The current research focuses on methods for generating synthetic data to simulate various operational scenarios or rare conveyor belt conditions. Due to the insufficient amount of training data for deep neural networks, a hybrid data augmentation method was implemented. For generating sequences of undamaged data, a TimeGAN model--a Generative Adversarial Network (GAN) designed for time series data--was used. Meanwhile, to create sequences representing damages, a signal mixing strategy was applied, combining randomly selected undamaged signals with strain gauge responses to belt damage. Damage detection was performed using an LSTM-AM (Long Short-Term Memory-Attention Model) algorithm, which identifies anomalies in strain gauge signals. Through data synchronization, the system was able to determine the exact location of the damage. The reasoning method is schematically presented in Figure 1.  
The novelty of the proposed solution lies in integrating advanced sequential data augmentation methods with modern deep learning algorithms for conveyor belt diagnostics. The hybrid data generation approach, combining TimeGAN with superposition of one-dimensional observation sequences, represents a breakthrough in addressing the challenge of limited sample availability in diagnostic research. Furthermore, applying the LSTM-AM algorithm to strain gauge signal analysis not only enables damage detection but also allows precise localization, which is crucial for improving conveyor belt safety and efficiency. The article is structured as follows: Section 1 presents the main problem and a literature review on conveyor belt damage detection methods. Section 2 discusses the deep learning algorithms used for data augmentation using signal superposition and anomaly detection. Section 2 describes the methodology for data collection in the experimental setup. Section 3 applies the described AI algorithms to the research objectives and discusses the results. Section 4 provides a summary and conclusions. Figure 1: Illustration the method of fault detection and localization.