# Using Hybrid LSTM Neural Networks to Detect Anomalies in the Fiber Tube Manufacturing Process
## 3 Results and Discussion
The system operator's task was to control and tune the line's working parameters so that the process state signature recognized by the LSTM network corresponded to its proper operation. The number of \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metric** & **LSTM** & **Random Forest** & **SVM** & **RNN** \\ \hline Precision & **>0.94** & \(\sim\)0.85 & \(\sim\)0.88 & \(\sim\)0.87 \\ \hline Recall & **>0.96** & \(\sim\)0.83 & \(\sim\)0.85 & \(\sim\)0.86 \\ \hline F–1 Score & **>0.91** & \(\sim\)0.84 & \(\sim\)0.86 & \(\sim\)0.86 \\ \hline \(R^{2}\) & **>0.86** & \(\sim\)0.85 & \(\sim\)0.85 & \(\sim\)0.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Summary of results for each forecasting model, taking into account Precision, Recall, F-1 Score and \(R^{2}\) indicators (the higher the values, the better the performance). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Precision** & **Recall** & **F1-Score** \\ \hline Proper & 0.9414 & 0.9695 & 0.9553 \\ \hline Anomaly 1 & 0.9180 & 0.9438 & 0.9307 \\ \hline Anomaly 2 & 0.8554 & 0.7513 & 0.7997 \\ \hline Anomaly 3 & 0.9185 & 0.9185 & 0.9185 \\ \hline Anomaly 4 & 0.6647 & 0.8248 & 0.7368 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance indicator results for each class. distinguishable anomalies for the RLM line was set at 4, based on heuristic assumptions and consultations with experts supervising the production technology on this line. During the period from 1 January 2024, to 28 June 2024, the constructed LSTM network model was test-integrated into the production process monitoring system. Based on the observations made, the following coincidences between detected anomalies and corrective actions taken were identified:  
Figure 11: Signalization of proper operation in the analysis of the technological line’s performance. Figure 10: Signalization of proper operation in the analysis of the technological line’s performance. * Anomaly 1: Excessive fluctuations in pressure and temperature alter the geometry and texture of the produced item. Significant pressure variations cause diameter changes within the range of 0.2 [mm] to 0.4 [mm], leading to the product being classified as non-compliant. In the cable coating extrusion process, large pressure changes result in discontinuities in the coating material, causing the final product to be divided into short segments, which are often unacceptable to customers. * Anomaly 2: Excessive deviations in the pressure and temperature of the hydrophobic gel. Pressure changes lead to variations in the external and internal diameters of the semi-finished product. Changes in the external diameter result in weakened strength at the constriction points of the semi-finished product. * Anomaly 3: Excessive production speed and the associated tension force of the production line and winding device. According to experts, this is a crucial anomaly that causes excess fiber in the tube, negatively affecting the transmission properties and strength of the finished fiber optic cable. Moreover, its frequent occurrence indicates wear and tear of the drive and consumable parts of the line. * Anomaly 4: Temperature fluctuations in the cooling bath water affect the surface condition of semi-finished and finished products, as well as the dynamics of secondary shrinkage, which negatively impacts semi-finished and finished cables many days after their production. During the analyzed period of the test implementation, a reduction in the amount of defective product by 66.3% was achieved, which should be considered a significant reduction in unnecessary production costs per unit length of the final product. A notable drawback of the developed approach is the relatively high sensitivity of the monitoring module to sterile measurement conditions that must be maintained when using such a large number of sensors. Its susceptibility to transient spikes or temporary loss of measurement values may pose a significant challenge in adapting the proposed method to similar production processes.