# Using Hybrid LSTM Neural Networks to Detect Anomalies in the Fiber Tube Manufacturing Process
## 1 Introduction
The authors achieved high accuracy in anomaly detection using datasets from various sensors, and the knowledge transfer between sensors enhanced system effectiveness in new, unfamiliar production environments. The study [10] proposed the creation of a digital twin for a production line, enabling improved monitoring and real-time anomaly prediction. This approach integrated digital twin modeling with machine learning algorithms, such as Long Short-Term Memory (LSTM) networks and autoencoders. The system demonstrated high accuracy in anomaly prediction, while the digital twin allowed for dynamic production parameter adjustments, reducing defects by 40% compared to standard methods. In [5], the authors introduced a system for predicting production costs in the context of Industry 4.0. This system integrated data from multiple sources, including industrial sensors and ERP systems, to forecast changes in production costs. Advanced ML models, such as LSTM networks and decision trees, were applied to analyze time-series data from various sources. The model achieved a 92% accuracy rate in production cost predictions, significantly reducing financial losses from unplanned downtimes and production defects. The study [7] focused on anomaly detection in optical fiber networks using machine learning algorithms. The primary application involved monitoring the integrity of optical cables and detecting damages in communication networks. Support Vector Machines (SVM) and neural networks, including LSTM models, were used to process sensor data and detect cable irregularities. The LSTM model excelled in real-time anomaly detection, achieving 94% accuracy, and effectively identified both minor and major damages. Finally, Ref. [12] investigated automatic anomaly detection in manufacturing machines based on sensor data, aiming to minimize machine failures and downtimes by predicting potential issues. Statistical algorithms such as linear regression and k-Nearest Neighbors (k-NN) were combined with advanced machine learning methods, including LSTM networks. These models successfully identified anomalies in 87% of cases, leading to a 25% reduction in production process downtimes. Table 1 presents a summary of the technologies employed and the performance of the models in the context of research on anomaly detection and production process monitoring. Regarding above, the industry practice shows, that often heuristic strategies are needed to overcome these industrial challenges, which frequently hinder effective detection [32; 33]. Emerging trends such as federated learning, knowledge graphs, and advanced neural architectures offer promising directions for overcoming these challenges. By integrating domain expertise with sophisticated computational techniques, anomaly detection continues to evolve, bridging gaps across diverse applications. Although the recent reviews have attempted to unify diverse methods to facilitate objective comparisons, but unfortunately significant challenges persist due to process-specific measurement differences and the unique nature of anomalies across domains, which often hinder generalization [34; 35; 36; 37; 38; 39; 40]. In this study, the authors refer to the concept of anomalies as a set of various disturbances that may accompany the production process. These deviations from the correct process, which has its proper signature representing a normal and unthreatened production state, can become, in the absence of corrective actions, a potential source of defects in the final product. This may result in either a reduction in the product's final quality or, in a worst-case scenario, render it unusable for further application. This paper presents the results obtained from monitoring the tube extrusion process based on actual measurements recorded on the production line at the industrial plant FIBRAIN Sp. z o.o.