Anomaly detection with a digital twin

Anomaly detection with a digital twin

overview

The behavior of large-scale distributed IoT systems is hard to verify and validate. The reasons include:

  1. The specification is often unclear, ambiguous and incomplete.

  2. It is humanly impossible to reason on the correctness of a system consisting of thousands of components.

  3. It is very hard to observe all related components when trying to solve a problem.

Get your specification right

In the OpenAIS project, ESI’s knowledge and experience in the application of Domain-Specific Languages (DSLs) was used to model the behavior of intelligent IoT lighting systems. We introduced generic IoT DSLs to specify unambiguous behavior models and added validation rules which assist in the creation of correct models.

The DSL models that capture the knowledge of the system are used to generate virtual prototypes. One of these virtual prototypes is a simulator which receives virtual sensor events and triggers virtual actuators according to the system specification. It is used to analyze system aspects such as behavior validation with the customer, energy consumption, and effects of message loss and network latencies. The models are adapted until analysis shows that the system behaves as expected by the customer.

Get your operational system right

We developed a digital twin that combines knowledge and operational data to monitor the behavior of the IoT lighting system. To achieve this, we transformed the generated virtual prototype into a digital twin by feeding it operational sensor data collected from the physical system. As a result, the digital twin control sits virtual actuators according to the system specification, generating reference actuator data.

Both operational actuator data and digital twin generated reference data are now available for digital twin applications to use.

In OpenAIS, we used the digital twin for behavioral anomaly detection in a Root Cause Analysis (RCA) approach. An application was developed to compare the operational actuator data to the reference actuator data. Based on the results from FMEA and HAZOP studies and the available data, a semi-automatic diagnosis and resolution method was developed and deployed.

The RCA approach was used in a pilot project on the fifth floor of the “Witte Dame” building in Eindhoven, where an OpenAIS intelligent lighting system with approximately 1,200 components was installed. The RCA approach proved to be very successful, as thousands of behavioral anomalies were detected, diagnosed and quickly resolved.

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