# Using Hybrid LSTM Neural Networks to Detect Anomalies in the Fiber Tube Manufacturing Process
## 1 Introduction
These methods focus on uncovering latent patterns within data without labeled training samples, providing significant insights into disease recognition while addressing specific anomaly types. The use of singular value decomposition (SVD) in deep neural network (DNN) models aids in encrypting hidden features of signals. Techniques incorporating Fourier transforms allow for robust analysis of anomalies while maintaining data integrity [16]. Recent developments include the implementation of self-adversarial variational autoencoders (VAEs) integrated with contrastive learning to enhance anomaly detection capabilities [16]. These architectures excel in capturing nuanced differences between normal and anomalous states. Multi-node knowledge graphs fused with expert knowledge and Bi-directional Long Short-Term Memory (Bi-LSTM) networks have been proposed [17] as powerful tools for local feature extraction in anomaly detection. This approach leverages both structural and temporal relationships within the data. The multi-scale sparse attention module effectively extracts global features across scales, preserving pattern information critical for detecting process disturbances [18]. This approach is particularly adept at handling data from industrial monitoring systems [19]. For multidimensional processes, local feature masking can amplify state differences to visually highlight anomalies [20]. Similarly, mechanisms for identifying highly divergent patterns based on data density help streamline the detection process [21]. A knowledge distillation approach [22], transferring insights from large models to smaller, more efficient models, has proven effective in speeding up anomaly detection. Stochasticapproaches utilizing partial Markov chains are employed to mask irrelevant process features, such as in image data [23]. These methods improve reconstruction quality and focus anomaly detection efforts on significant features. An imbalance in training datasets is a persistent issue, especially in industrial processes where generating anomalies artificially is infeasible [24]. Few-shot anomaly detection using positive-unlabeled learning addresses this by preparing well-structured datasets, albeit with challenges in real-world implementation [25]. Preprocessing transformations, such as Kalman filtering, are used to simplify input data for anomaly detection in diverse systems, including ECG signals and cooling devices [26]. Additionally, federated learning enhances defect detection, achieving high accuracy, precision, recall, and F1 scores in certain industrial settings [24]. Frequency-domain techniques generate augmented pseudo-anomalous images, allowing for the discovery of key anomaly characteristics [27]. Analyzing short video sequences of industrial processes has achieved notable success, such as the MSTE environment yielding 90.7% accuracy [28]. However, video-based methods face limitations due to measurement constraints and environmental variability. In satellite telemetry anomaly detection, deep learning algorithms contend with significant noise and data gaps [29]. To address data scarcity in anomaly states, mechanisms such as feature injection and heterogeneous spatiotemporal graphs have been developed [30]. These techniques enable artificial supplementation of training datasets, improving detection accuracy in processes with high anomaly deficits. In unsupervised scenarios, attributed networks leveraging graph-based models enhance the differentiation between normal states and anomalies by reducing interference [31]. In the study [6], anomaly detection in smart factories was explored using machine learning techniques, particularly addressing challenges related to diverse sensors and their transferability across different production lines. A combination of machine learning algorithms, including neural networks and transfer learning, was employed to facilitate knowledge transfer between production lines.