## 4 Conclusions and Future Work  
In the conducted studies, a novel method for detecting and localizing conveyor belt damages was presented, utilizing the LSTM-AM neural network and a hybrid data augmentation method. The results showed that using the TimeGAN model to generate synthetic data for undamaged cases, along with a superposition of one-dimensional observation sequences for augmenting damage data, effectively increased the number of training samples, which translated into improved model performance. The LSTM-AM model accurately identified anomalies in strain gauge signals, allowing for precise localization of damages. Analysis of the results revealed that the largest signal changes occurred in strain gauge T2, particularly in cases of DL2, DL3, DL4, and DT1 damages. Incorporating an attention mechanism into the LSTM network reduced the number of false alarms, thereby enhancing both precision and recall compared to the traditional LSTM version. RMSE error histograms confirmed the model's effectiveness in detecting damages. Signal reconstruction by the LSTM-AM model more accurately reproduced the original waveforms than the standard LSTM, significantly enhancing detection accuracy. Additionally, DT2 and DT3 damages at the belt edge caused a reduction in tension and destabilization of the conveyor's operation, which could lead to its stoppage. The presented solution represents a significant advancement in conveyor belt diagnostics, offering a holistic approach to early damage detection and improved operational reliability. Its uniqueness lies in the synergistic integration of key innovations:  
* Hybrid data augmentation effectively overcomes the fundamental problem of fault data scarcity, enabling the efficient training of robust deep learning models. * The cost-effective strain gauge system provides continuous, precise real-time measurements of loads and stresses, standing out for its low implementation and operational costs. Its main advantage is eliminating the need for structural intervention in the belt or stopping the conveyor, which allows for the early detection of subtle changes signaling belt deformation, pulley misalignment, or uneven wear. * Precise real-time damage localization, particularly in textile belts, fills a significant gap in existing methods. * The LSTM network with an attention mechanism (LSTM-AM) enhances system robustness, improving detection accuracy and reducing false alarms in complex time series data. It is important to emphasize that while the strain gauge system does not directly measure belt thickness or provide the same precision in tear localization as more expensive ultrasonic or laser methods, its strength lies in acting as an effective and budget-friendly early warning tool. It enables immediate anomaly detection, allowing for preventative measures to be taken before a minor issue escalates into a major failure or unplanned downtime. The ability to prevent \"destabilization\" and \"stoppage\" is direct evidence of the system's proactive nature in averting serious operational consequences. The current study primarily focused on a specific type of conveyor belt, a welded PVC (polyvinyl chloride) and textile core conveyor belt, within a controlled laboratory setting. While the underlying principles of strain gauge monitoring, hybrid data augmentation, and LSTM-AM for time-series anomaly detection have yielded good results, the direct transferability of the trained model to drastically different belt materials, constructions, or operational environments requires further investigation. Several factors contribute to these applicability challenges. One such factor is material properties. Different conveyor belt materials, such as rubber, steel cord, or various fabric plies, exhibit distinct mechanical properties, including stiffness and damping characteristics. These differences will significantly alter how damages propagate and how strain gauges respond to those damages. The current model's learned patterns are specific to the mechanical response of the PVC and textile core belt.