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

Castellani, Andrea; Schmitt, Sebastian; Squartini, Stefano

doi:10.1109/tii.2020.3019788

Cited by 95 publications

(31 citation statements)

References 24 publications

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“…In the early stage of the machine tool processing program design and production preparation, the CNC machine debugging cycle is a long, risky, and high investment. At the same time, the CNC machine tool technicians also have a high level of technical requirements [12]. erefore, in the actual production process, the CNC machine tool machining process design errors will bring serious processing accidents [13].…”

Section: Related Workmentioning

confidence: 99%

Numerical Analysis of Digital Twin System Modeling Methods Aided by Graph‐Theoretic Combinatorial Optimization

Zhou

2022

Discrete Dynamics in Nature and Society

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This paper combines the digital twin system modeling method to conduct an in-depth study and analysis of graph-theoretic combinatorial optimization. This paper provides new ideas and approaches for optimal numerical analysis work by studying the digital twin modeling method that integrates digital modeling and graph theory combination, provides theoretical support for safe, stable, and economic operation of the system, proposes a solution for digital twin model based on big data platform, focuses on the nearest neighbor propagation (AP) and graph theory combination, solves the digital twin real-time monitoring data asynchronous, incomplete problem, and applies the algorithm to the digital twin model based on the big data platform for data preprocessing to achieve better results. This paper also presents a web-based digital twin system based on intelligent practical needs, analysis, and comparison of existing models, combined with digital twin technology, detailing the differences and connections between the various levels of numerical analysis and the implementation of this data in various fields, such as user management, equipment health management, product quality management, and workshop 3D navigation and detailed modeling of the digital twin system based on this numerical analysis to realize remote online monitoring, analysis, and management. In this paper, for the numerical analysis process, firstly, the key technologies of modeling and simulation operation control of production line based on digital twin are studied, and the rapid response manufacturing system based on a digital twin is designed and validated. Secondly, a scheduling technology framework for capacity simulation evaluation and optimization is established, and batching optimization, outsourcing decision, and rolling scheduling techniques are thus proposed to form a batching optimization algorithm based on priority rules, which realizes batching processing, outsourcing decision, and rolling scheduling of production orders to optimize equipment utilization and capacity. Finally, digital twin-based modeling is designed, and the validation results demonstrate the system’s superior performance in achieving information interaction between physical and virtual production lines, optimization of numerical analysis, and display of results.

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Section: Related Workmentioning

confidence: 99%

Numerical Analysis of Digital Twin System Modeling Methods Aided by Graph‐Theoretic Combinatorial Optimization

Zhou

2022

Discrete Dynamics in Nature and Society

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“…The study developed by Castellani [25] demonstrates its use in order to obtain the DT to both generate normal data in order to simulate the system to be studied and later use it to detect anomalies.…”

Section: Related Workmentioning

confidence: 99%

Digital Twins in Solar Farms: An Approach through Time Series and Deep Learning

Arafet

Berlanga

2021

Algorithms

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The generation of electricity through renewable energy sources increases every day, with solar energy being one of the fastest-growing. The emergence of information technologies such as Digital Twins (DT) in the field of the Internet of Things and Industry 4.0 allows a substantial development in automatic diagnostic systems. The objective of this work is to obtain the DT of a Photovoltaic Solar Farm (PVSF) with a deep-learning (DL) approach. To build such a DT, sensor-based time series are properly analyzed and processed. The resulting data are used to train a DL model (e.g., autoencoders) in order to detect anomalies of the physical system in its DT. Results show a reconstruction error around 0.1, a recall score of 0.92 and an Area Under Curve (AUC) of 0.97. Therefore, this paper demonstrates that the DT can reproduce the behavior as well as detect efficiently anomalies of the physical system.

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“…For example, Ref. [29] presents a data-driven approach to realize a digital twin for anomaly detection using weakly supervised learning. It is important to note that the digital twin itself does not possess the intelligence to take decisions that affect the physical system.…”

Section: Definition Of a Digital Twinmentioning

confidence: 99%

Digital Twins for Wind Energy Conversion Systems: A Literature Review of Potential Modelling Techniques Focused on Model Fidelity and Computational Load

et al. 2021

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The Industry 4.0 concept of a Digital Twin will bring many advantages for wind energy conversion systems, e.g., in condition monitoring, predictive maintenance and the optimisation of control or design parameters. A virtual replica is at the heart of a digital twin. To construct a virtual replica, appropriate modelling techniques must be selected for the turbine components. These models must be chosen with the intended use case of the digital twin in mind, finding a proper balance between the model fidelity and computational load. This review article presents an overview of the recent literature on modelling techniques for turbine aerodynamics, structure and drivetrain mechanics, the permanent magnet synchronous generator, the power electronic converter and the pitch and yaw systems. For each component, a balanced overview is given of models with varying model fidelity and computational load, ranging from simplified lumped parameter models to advanced numerical Finite Element Method (FEM)-based models. The results of the literature review are presented graphically to aid the reader in the model selection process. Based on this review, a high-level structure of a digital twin is proposed together with a virtual replica with a minimum computational load. The concept of a multi-level hierarchical virtual replica is presented.

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

Cited by 95 publications

References 24 publications

Numerical Analysis of Digital Twin System Modeling Methods Aided by Graph‐Theoretic Combinatorial Optimization

Numerical Analysis of Digital Twin System Modeling Methods Aided by Graph‐Theoretic Combinatorial Optimization

Digital Twins in Solar Farms: An Approach through Time Series and Deep Learning

Digital Twins for Wind Energy Conversion Systems: A Literature Review of Potential Modelling Techniques Focused on Model Fidelity and Computational Load

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