# Real-World Anomaly Detection by using Digital Twin Systems and Weakly-Supervised Learning

arXiv: https://arxiv.org/abs/2011.06296 · HTML: https://mlsanity.com/paper/2011.06296/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2020.

## Built on by (most cited first, 17 of 17)

- [A Survey of Graph-based Deep Learning for Anomaly Detection in Distributed Systems](https://mlsanity.com/paper/2206.04149/) (2022)
- [Deep Video Anomaly Detection: Opportunities and Challenges](https://mlsanity.com/paper/2110.05086/) (2021)
- [Holistic Network Virtualization and Pervasive Network Intelligence for 6G](https://mlsanity.com/paper/2301.00519/) (2023)
- [A Review of Digital Twins and their Application in Cybersecurity based on Artificial Intelligence](https://mlsanity.com/paper/2311.01154/) (2023)
- [Towards Digital Twin Oriented Modelling of Complex Networked Systems and Their Dynamics: A Comprehensive Survey](https://mlsanity.com/paper/2202.09363/) (2022)
- [Task-Sensitive Concept Drift Detector with Constraint Embedding](https://mlsanity.com/paper/2108.06980/) (2021)
- [GDFlow: Anomaly Detection with NCDE-based Normalizing Flow for Advanced Driver Assistance System](https://mlsanity.com/paper/2409.05346/) (2024)
- [Defect detection using weakly supervised learning](https://mlsanity.com/paper/2303.15092/) (2023)
- [Siamese Neural Networks for Skin Cancer Classification and New Class Detection using Clinical and Dermoscopic Image Datasets](https://mlsanity.com/paper/2212.06130/) (2022)
- [Benchmarking Anomaly Detection Algorithms: Deep Learning and Beyond](https://mlsanity.com/paper/2402.07281/) (2024)
- [Learning Paradigms and Modelling Methodologies for Digital Twins in Process Industry](https://mlsanity.com/paper/2407.02275/) (2024)
- [A Real-World Energy Management Dataset from a Smart Company Building for Optimization and Machine Learning](https://mlsanity.com/paper/2503.11469/) (2025)
- [Weather-Driven Agricultural Decision-Making Using Digital Twins Under Imperfect Conditions](https://mlsanity.com/paper/2508.08326/) (2025)
- [P-WAE: Generalized Patch-Wasserstein Autoencoder for Anomaly Screening](https://mlsanity.com/paper/2108.03815/) (2021)
- [iADCPS: Time Series Anomaly Detection for Evolving Cyber-physical Systems via Incremental Meta-learning](https://mlsanity.com/paper/2504.04374/) (2025)
- [Industrial Energy Disaggregation with Digital Twin-generated Dataset and Efficient Data Augmentation](https://mlsanity.com/paper/2506.20525/) (2025)
- [Digital Twin Degradation: Detecting Cyber Physical Attacks via Temporal Inconsistencies](https://mlsanity.com/paper/2608.16159/) (2026)

## Abstract (arXiv)

The continuously growing amount of monitored data in the Industry 4.0 context requires strong and reliable anomaly detection techniques. The advancement of Digital Twin technologies allows for realistic simulations of complex machinery, therefore, it is ideally suited to generate synthetic datasets for the use in anomaly detection approaches when compared to actual measurement data. In this paper, we present novel weakly-supervised approaches to anomaly detection for industrial settings. The approaches make use of a Digital Twin to generate a training dataset which simulates the normal operation of the machinery, along with a small set of labeled anomalous measurement from the real machinery. In particular, we introduce a clustering-based approach, called Cluster Centers (CC), and a neural architecture based on the Siamese Autoencoders (SAE), which are tailored for weakly-supervised settings with very few labeled data samples. The performance of the proposed methods is compared against various state-of-the-art anomaly detection algorithms on an application to a real-world dataset from a facility monitoring system, by using a multitude of performance measures. Also, the influence of hyper-parameters related to feature extraction and network architecture is investigated. We find that the proposed SAE based solutions outperform state-of-the-art anomaly detection approaches very robustly for many different hyper-parameter settings on all performance measures.