# Using Hybrid LSTM Neural Networks to Detect Anomalies in the Fiber Tube Manufacturing Process
## 1 Introduction  
Fiber optic cables in modern civilization are the primary medium used for efficient and reliable information transmission. The continuous expansion of computer network infrastructure drives a growing demand for such products. Solutions are expected to include both long segments of optical medium as well as multi-core and multi-mode solutions, which allow for parallelization and thus acceleration of information transmission. The basic raw material is usually optical fiber, which has specific parameters describing its attenuation, rigidity, and physical dimensions. It is obtained by process of drawing the fiber in special towers from prepared glass billets of high chemical purity, which ensures appropriately low optical attenuation. Typically, at this stage, the fiber is coated with a dedicated polymer that ensures light confinement within it. The polymer layers can be combined with a color that ensures proper separation of fibers already at the stage of digital network construction. The next stage of optical cable manufacturing in industrial conditions is the extrusion process of the tube, which involves continuous heating and extrusion of plastic or metal materials to form hollow tubes that serve as a hydrophobiccoating surrounding bundles of optical fibers. The subsequent step is the twisting of tube assemblies, and the final product of this process is an optical cable. As can be seen, the above process is complex, with many different state coordinates, and each stage of production can potentially be a source of defects in the final product. In this context, predicting process anomalies using data from production equipment and taking preventive action is crucial. This is a key leitmotif that motivates many research teams around the world [1; 2; 3] to seek solutions and tools for monitoring this process and preventing defects that degrade the quality of the final product. Typically, the anomaly detection process involves collecting and analyzing data from sensors that monitor temperature, pressure, vibrations, chemical parameters, diameter, ovality, and other indicators specific to a given production line's state. In recent years, statistical algorithms (e.g., linear regression), machine learning algorithms (e.g., decision trees, Support Vector Machines (SVM), neural networks [2; 4]), and deep learning algorithms (e.g., Recurrent Neural Networks (RNN), Autoencoders) have been used for this purpose. The primary task of these algorithms is to monitor the given process in real-time, maintain predictive maintenance [5], and ensure quality control. Currently, companies in the fiber optic cable manufacturing sector are developing various solutions to minimize the percentage of products manufactured out of specification. Among the challenges they face are the scalability and adaptability of algorithms, their ability to integrate with production systems, and their effectiveness in minimizing waste and downtime [6; 7; 8; 9; 10; 11; 12]. By reviewing the latest literature, State-of-the-Art Review on Anomaly Detection in Various Domains [13], several leading themes can be distinguished. The detection of anomalies has emerged as a critical focus across diverse domains, particularly within the Internet of Things (IoT), biomedical signal processing, industrial processes, and satellite telemetry data. This review synthesizes recent advances in methods and approaches, highlighting their unique characteristics, applications, and associated challenges. Anomaly detection in IoT systems often employs dual-view mechanisms, combining time reconstructors, frequency reconstructors, and dual-view adversarial learning [14; 15]. These methods aim to analyze data streams comprehensively, addressing both temporal and spectral dimensions to enhance detection accuracy. Biomedical data, such as signals related to [PERSON]'s disease, are processed using unsupervised deep networks.