Abstract:
As Digital Twins gain more traction and their adoption in industry increases, there
is a need to integrate such technology with machine learning features to enhance functionality
and enable decision making tasks. This has lead to the emergence of a concept known as Digital
Triplet; an enhancement of Digital Twin technology through the addition of an ’intelligent activity
layer’. This is a relatively new technology in Industrie 4.0 and research efforts are geared towards
exploring its applicability, development and testing of means for implementation and quick adoption.
This paper presents the design and implementation of a Digital Triplet for a three-floor elevator
system. It demonstrates the integration of a machine learning (ML) object detection model and the
system Digital Twin. This was done to introduce an additional security feature that enabled the system
to make a decision, based on objects detected and take preliminary security measures. The virtual
model was designed in Siemens NX and programmed via Total Integrated Automation (TIA) portal
software. The corresponding physical model was fabricated and controlled using a Programmable
Logic Controller (PLC) S7 1200. A control program was developed to mimic the general operations
of a typical elevator system used in a commercial building setting. Communication, between the
physical and virtual models, was enabled using the OPC-Unified Architecture (OPC-UA) protocol.
Object recognition using “You only look once” (YOLOV3) based machine learning algorithm was
incorporated. The Digital Triplet’s functionality was tested, ensuring the virtual system duplicated
actual operations of the physical counterpart through the use of sensor data. Performance testing was
done to determine the impact of the ML module on the real-time functionality aspect of the system.
Experiment results showed the object recognition contributed an average of 1.083 s to an overall
signal travel time of 1.338 s.
