Digital Twins for Anomaly Detection in the Industrial Internet of Things : Conceptual Architecture and Proof-of-Concept

• Modern cyber-physical systems based on the industrial Internet of Things (IIoT) can be highly distributed and heterogeneous, and that increases the risk of failures due to misbehavior of interconnected components, or other interaction anomalies. In this article, we introduce a conceptual architecture for IIoT anomaly detection based on the paradigms of digital twins (DT) and autonomic computing (AC), and we test it through a proof-of-concept of industrial relevance. The architecture is derived from the current state-of-the-art in DT research and leverages on the MAPE-K feedback loop of AC in order to monitor, analyze, plan, and execute appropriate reconfiguration or mitigation strategies based on the detected deviation from prescriptive behavior stored as shared knowledge. We demonstrate the approach and discuss results by using a reference operational scenario of adequate complexity and criticality within the European Railway Traffic Management System.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)

Nyckelord

Anomaly detection

autonomic computing (AC)

cyber-physical systems

digital twins (DTs)

industrial Internet of Things (IIoT)

process mining (PM)

Behavioral research

Cyber Physical System

Embedded systems

Internet of things

Railroad transportation

Railroads

Autonomic Computing

Behavioral science

Conceptual architecture

Cybe-physical systems

Industrial internet of thing

Process mining

Computer architecture

Publikations- och innehållstyp

ref (ämneskategori)

art (ämneskategori)