Castellani A, Schmitt S, Squartini S. Real-World Anomaly Detection by Using Digital Twin Systems and Weakly Supervised Learning. IEEE Transactions on Industrial Informatics. 2021;17(7):4733-4742.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 article, we present novel weakly supervised approaches to anomaly detection for industrial settings. The approaches make use of a Digital Twin to generate a trai...
Detecting faults and anomalies in real-time industrial systems is a challenge due to the difficulty ...
We consider the problem of anomaly detection with a small set of partially labeled anomaly examples ...
International audienceEarly detection of anomalies in data centers is important to reduce downtimes ...
The continuously growing amount of monitored data in the Industry 4.0 context requires strong and re...
The continuously growing amount of monitored data in the Industry 4.0 context requires strong and re...
The continuously growing amount of monitored data in the Industry 4.0 context requires strong and re...
The continuously growing amount of monitored data in the Industry 4.0 context requires strong and re...
Cyber-Physical Systems (CPS) are susceptible to various anomalies during their operations. Thus, it ...
This work describes a structured solution that integrates digital twin models, machine-learning algo...
This work describes a structured solution that integrates digital twin models, machine-learning algo...
Anomaly detection in supercomputers is a very difficult problem due to the big scale of the systems ...
International audienceThis paper proposes a novel anomaly detection methodology for industrial syste...
International audienceThis paper proposes a novel anomaly detection methodology for industrial syste...
The cyber-physical security of Industrial Control Systems (ICSs) represents an actual and worthwhile...
In recent years, machine learning (ML) based digital twins (DTs) have seen widespread application in...
Detecting faults and anomalies in real-time industrial systems is a challenge due to the difficulty ...
We consider the problem of anomaly detection with a small set of partially labeled anomaly examples ...
International audienceEarly detection of anomalies in data centers is important to reduce downtimes ...
The continuously growing amount of monitored data in the Industry 4.0 context requires strong and re...
The continuously growing amount of monitored data in the Industry 4.0 context requires strong and re...
The continuously growing amount of monitored data in the Industry 4.0 context requires strong and re...
The continuously growing amount of monitored data in the Industry 4.0 context requires strong and re...
Cyber-Physical Systems (CPS) are susceptible to various anomalies during their operations. Thus, it ...
This work describes a structured solution that integrates digital twin models, machine-learning algo...
This work describes a structured solution that integrates digital twin models, machine-learning algo...
Anomaly detection in supercomputers is a very difficult problem due to the big scale of the systems ...
International audienceThis paper proposes a novel anomaly detection methodology for industrial syste...
International audienceThis paper proposes a novel anomaly detection methodology for industrial syste...
The cyber-physical security of Industrial Control Systems (ICSs) represents an actual and worthwhile...
In recent years, machine learning (ML) based digital twins (DTs) have seen widespread application in...
Detecting faults and anomalies in real-time industrial systems is a challenge due to the difficulty ...
We consider the problem of anomaly detection with a small set of partially labeled anomaly examples ...
International audienceEarly detection of anomalies in data centers is important to reduce downtimes ...