# Using Hybrid LSTM Neural Networks to Detect Anomalies in the Fiber Tube Manufacturing Process
## 4 Conclusions  
Combining statistical methods, machine learning, and sensor-based monitoring provides a robust approach to detecting and addressing anomalies. The proposed approach effectively integrates the multi-resolution K-means clustering method with recursive LSTM network architectures. The analysis conducted in this study on the available data from the monitored RLM production line identified key parameters determining product quality during technological processes: pressure and temperature of the material and gel, and constant production speed, which is crucial for maintaining the correct dimensions of the extruded product. The achieved results indicate the validity of using deep learning with LSTM networks for the analysis and classification of technological data.  
This work makes a significant contribution to the field of anomaly detection by applying an innovative hybrid architecture that combines clustering techniques with LSTM networks, enabling the automatic creation of training datasets even in the absence of manually labeled data. The multi-channel temporal data analysis system from production lines provided a new perspective on real-time monitoring of industrial processes, which can also be adapted to other industries. However, certain limitations of the developed model should be noted. The high sensitivity of the system to input data disturbances, particularly when using multiple sensors, necessitates precise measurement conditions. Additionally, the scalability of the model presents a challenge when deploying it across production lines with differing characteristics. Despite these challenges, the proposed method significantly reduces the number of defects and allows for better management of the production process.  
The ultimate application of the described system is full integration with production lines in real industrial facilities, enabling further monitoring and optimization of the manufacturing processes for optical fiber tubes. The system facilitates a significant reduction in waste and improvement in the quality of final products, making an essential contribution both economically and environmentally. The application of anomaly detection methods using LSTM in other manufacturing processes, especially within the Industry 4.0 framework, can enhance quality control across a wide range of technologies. Ultimately, the findings of this study demonstrate that further advancements in optical cable production technology and advanced machine learning techniques will continue to improve the precision and efficiency of anomaly detection methods, thus supporting better quality control and the advancement of industrial process automation.  
Conceptualization, Z.G.; methodology, Z.G.; software, Z.G. and E.Z.; validation, Z.G. and E.Z.; formal analysis, Z.G.; investigation, Z.G., E.Z. and L.O.; resources, L.O.; data curation, Z.G.; writing--original draft preparation, Z.G. and E.Z.; visualization, Z.G. and E.Z.; supervision, Z.G.; project administration, Z.G.; funding acquisition, L.O., Z.G. and E.Z. All authors have read and agreed to the published version of the manuscript.  
This project received funding from the National Centre for Research and Development under grant agreement No. POIR.01.01.01-00-1425/20.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
We gratefully acknowledge the support of the NCBR, as part of the competition: 6/1.1.1/2020 6/1.1.1/2020 SS Duze/MSP/JN 4, project number POIR.01.01.01-00-1425/20-00 and Fibrain Sp. z.o.o, which made this research possible.  
[PERSON] was employed by the company FIBRAIN Sp. z o.o. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.