# A Survey on Anomaly Detection for Technical Systems using LSTM Networks

arXiv: https://arxiv.org/abs/2105.13810 · HTML: https://mlsanity.com/paper/2105.13810/

## 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.

## Builds on (most cited first, 16 of 16)

- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks](https://mlsanity.com/paper/1703.07015/) (2017)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Progress & Compress: A scalable framework for continual learning](https://mlsanity.com/paper/1805.06370/) (2018)
- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)
- [Deep Learning for Anomaly Detection: A Survey](https://mlsanity.com/paper/1901.03407/) (2019)
- [A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencoder](https://mlsanity.com/paper/1711.00614/) (2017)
- [Graph-based Anomaly Detection and Description: A Survey](https://mlsanity.com/paper/1404.4679/) (2014)
- [Deep Learning for Medical Anomaly Detection -- A Survey](https://mlsanity.com/paper/2012.02364/) (2020)
- [Efficient GAN-Based Anomaly Detection](https://mlsanity.com/paper/1802.06222/) (2018)
- [Soft + Hardwired Attention: An LSTM Framework for Human Trajectory Prediction and Abnormal Event Detection](https://mlsanity.com/paper/1702.05552/) (2017)
- [Collective Anomaly Detection based on Long Short Term Memory Recurrent Neural Network](https://mlsanity.com/paper/1703.09752/) (2017)
- [Regularization-based Continual Learning for Anomaly Detection in Discrete Manufacturing](https://mlsanity.com/paper/2101.00509/) (2021)
- [Deep Transfer Learning for Industrial Automation: A Review and Discussion of New Techniques for Data-Driven Machine Learning](https://mlsanity.com/paper/2012.03301/) (2020)
- [ReRe: A Lightweight Real-time Ready-to-Go Anomaly Detection Approach for Time Series](https://mlsanity.com/paper/2004.02319/) (2020)

## Built on by (most cited first, 27 of 27)

- [A Comprehensive Survey on Process-Oriented Automatic Text Summarization with Exploration of LLM-Based Methods](https://mlsanity.com/paper/2403.02901/) (2024)
- [Deep Learning Advancements in Anomaly Detection: A Comprehensive Survey](https://mlsanity.com/paper/2503.13195/) (2025)
- [Online Model-based Anomaly Detection in Multivariate Time Series: Taxonomy, Survey, Research Challenges and Future Directions](https://mlsanity.com/paper/2408.03747/) (2024)
- [A novel knowledge graph development for industry design: A case study on indirect coal liquefaction process](https://mlsanity.com/paper/2111.13854/) (2021)
- [Memory-free Online Change-point Detection: A Novel Neural Network Approach](https://mlsanity.com/paper/2207.03932/) (2022)
- [Towards Deep Industrial Transfer Learning for Anomaly Detection on Time Series Data](https://mlsanity.com/paper/2106.04920/) (2021)
- [FLEX: FLEXible Federated Learning Framework](https://mlsanity.com/paper/2404.06127/) (2024)
- [A Bi-LSTM Autoencoder Framework for Anomaly Detection -- A Case Study of a Wind Power Dataset](https://mlsanity.com/paper/2303.09703/) (2023)
- [HRGCN: Heterogeneous Graph-level Anomaly Detection with Hierarchical Relation-augmented Graph Neural Networks](https://mlsanity.com/paper/2308.14340/) (2023)
- [Hybrid AI-based Anomaly Detection Model using Phasor Measurement Unit Data](https://mlsanity.com/paper/2209.12665/) (2022)
- [HFN: Heterogeneous Feature Network for Multivariate Time Series Anomaly Detection](https://mlsanity.com/paper/2211.00277/) (2022)
- [Coincident Learning for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2301.11368/) (2023)
- [Comparative Study on Semi-supervised Learning Applied for Anomaly Detection in Hydraulic Condition Monitoring System](https://mlsanity.com/paper/2306.02709/) (2023)
- [Detection Latencies of Anomaly Detectors: An Overlooked Perspective ?](https://mlsanity.com/paper/2402.09082/) (2024)
- [AR-Pro: Counterfactual Explanations for Anomaly Repair with Formal Properties](https://mlsanity.com/paper/2410.24178/) (2024)
- [Federated Quantum Kernel Learning for Anomaly Detection in Multivariate IoT Time-Series](https://mlsanity.com/paper/2511.02301/) (2025)
- [RAMSeS: Robust and Adaptive Model Selection for Time-Series Anomaly Detection Algorithms](https://mlsanity.com/paper/2602.21766/) (2026)
- [Privacy-preserving household load forecasting based on non-intrusive load monitoring: A federated deep learning approach](https://mlsanity.com/paper/2206.15192/) (2022)
- [Topics in Deep Learning and Optimization Algorithms for IoT Applications in Smart Transportation](https://mlsanity.com/paper/2210.07246/) (2022)
- [A Survey on Deep Industrial Transfer Learning in Fault Prognostics](https://mlsanity.com/paper/2301.01705/) (2023)
- [CyPhERS: A Cyber-Physical Event Reasoning System providing real-time situational awareness for attack and fault response](https://mlsanity.com/paper/2305.16907/) (2023)
- [Acquiring Better Load Estimates by Combining Anomaly and Change Point Detection in Power Grid Time-series Measurements](https://mlsanity.com/paper/2405.16164/) (2024)
- [Large Language Model-empowered multimodal strain sensory system for shape recognition, monitoring, and human interaction of tensegrity](https://mlsanity.com/paper/2406.10264/) (2024)
- [Advancing climate model interpretability: Feature attribution for Arctic melt anomalies](https://mlsanity.com/paper/2502.07741/) (2025)
- [Data-Driven Heat Pump Management: Combining Machine Learning with Anomaly Detection for Residential Hot Water Systems](https://mlsanity.com/paper/2506.15719/) (2025)
- [Multivariate time-series forecasting of ASTRI-Horn monitoring data: A Normal Behavior Model](https://mlsanity.com/paper/2602.19984/) (2026)
- [ASTRO: Adaptive Spatio-Temporal Reinforcement Optimization for GNN Powered Anomly Detection in Cyber Physical Systems](https://mlsanity.com/paper/2605.25135/) (2026)

## 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.