mlsanityData to 14 Oct 2026

A Survey on Anomaly Detection for Technical Systems using LSTM Networks

arXiv 2105.13810 ↗First posted 28 May 2021cs.LG cs.AI stat.MLcited by 27

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Abstract (arXiv)

Anomalies represent deviations from the intended system operation and can lead to decreased efficiency as well as partial or complete system failure. As the causes of anomalies are often unknown due to complex system dynamics, efficient anomaly detection is necessary. Conventional detection approaches rely on statistical and time-invariant methods that fail to address the complex and dynamic nature of anomalies. With advances in artificial intelligence and increasing importance for anomaly detection and prevention in various domains, artificial neural network approaches enable the detection of more complex anomaly types while considering temporal and contextual characteristics. In this article, a survey on state-of-the-art anomaly detection using deep neural and especially long short-term memory networks is conducted. The investigated approaches are evaluated based on the application scenario, data and anomaly types as well as further metrics. To highlight the potential of upcoming anomaly detection techniques, graph-based and transfer learning approaches are also included in the survey, enabling the analysis of heterogeneous data as well as compensating for its shortage and improving the handling of dynamic processes.

Key facts. Cited by 27 papers in the map (data through 14 Oct 2026). Builds on 16 of its 54 references that are in the map. First posted on arXiv 28 May 2021.

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16 of 54 references.

References · 16

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  8. 2017A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencodercited by 209
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4 more
  1. 2017Collective Anomaly Detection based on Long Short Term Memory Recurrent Neural Networkcited by 28
  2. 2021Regularization-based Continual Learning for Anomaly Detection in Discrete Manufacturingcited by 19
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 2; 2022: 5; 2023: 6; 2024: 7; 2025: 4; 2026: 3.

27 papers.

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  1. 2024A Comprehensive Survey on Process-Oriented Automatic Text Summarization with Exploration of LLM-Based Methodscited by 102
  2. 2025Deep Learning Advancements in Anomaly Detection: A Comprehensive Surveycited by 17
  3. 2024Online Model-based Anomaly Detection in Multivariate Time Series: Taxonomy, Survey, Research Challenges and Future Directionscited by 6
  4. 2021A novel knowledge graph development for industry design: A case study on indirect coal liquefaction processcited by 5
  5. 2022Memory-free Online Change-point Detection: A Novel Neural Network Approachcited by 5
  6. 2021Towards Deep Industrial Transfer Learning for Anomaly Detection on Time Series Datacited by 4
  7. 2024FLEX: FLEXible Federated Learning Frameworkcited by 4
  8. 2023A Bi-LSTM Autoencoder Framework for Anomaly Detection -- A Case Study of a Wind Power Datasetcited by 2
  9. 2023HRGCN: Heterogeneous Graph-level Anomaly Detection with Hierarchical Relation-augmented Graph Neural Networkscited by 2
  10. 2022Hybrid AI-based Anomaly Detection Model using Phasor Measurement Unit Datacited by 1
  11. 2022HFN: Heterogeneous Feature Network for Multivariate Time Series Anomaly Detectioncited by 1
  12. 2023Coincident Learning for Unsupervised Anomaly Detectioncited by 1
15 more
  1. 2023Comparative Study on Semi-supervised Learning Applied for Anomaly Detection in Hydraulic Condition Monitoring Systemcited by 1
  2. 2024Detection Latencies of Anomaly Detectors: An Overlooked Perspective ?cited by 1
  3. 2024AR-Pro: Counterfactual Explanations for Anomaly Repair with Formal Propertiescited by 1
  4. 2025Federated Quantum Kernel Learning for Anomaly Detection in Multivariate IoT Time-Seriescited by 1
  5. 2026RAMSeS: Robust and Adaptive Model Selection for Time-Series Anomaly Detection Algorithmscited by 1
  6. 2022Privacy-preserving household load forecasting based on non-intrusive load monitoring: A federated deep learning approachcited by 0
  7. 2022Topics in Deep Learning and Optimization Algorithms for IoT Applications in Smart Transportationcited by 0
  8. 2023A Survey on Deep Industrial Transfer Learning in Fault Prognosticscited by 0
  9. 2023CyPhERS: A Cyber-Physical Event Reasoning System providing real-time situational awareness for attack and fault responsecited by 0
  10. 2024Acquiring Better Load Estimates by Combining Anomaly and Change Point Detection in Power Grid Time-series Measurementscited by 0
  11. 2024Large Language Model-empowered multimodal strain sensory system for shape recognition, monitoring, and human interaction of tensegritycited by 0
  12. 2025Advancing climate model interpretability: Feature attribution for Arctic melt anomaliescited by 0
  13. 2025Data-Driven Heat Pump Management: Combining Machine Learning with Anomaly Detection for Residential Hot Water Systemscited by 0
  14. 2026Multivariate time-series forecasting of ASTRI-Horn monitoring data: A Normal Behavior Modelcited by 0
  15. 2026ASTRO: Adaptive Spatio-Temporal Reinforcement Optimization for GNN Powered Anomly Detection in Cyber Physical Systemscited by 0