@article{GichaneByiringiroChesangetal.2020,
  author    = {Michael M. Gichane and Jean B. Byiringiro and Andrew K. Chesang and Peterson M. Nyaga and Rogers K. Langat and Hasan Smajic and Consolata W. Kiiru},
  title     = {Digital Triplet Approach for Real-Time Monitoring and Control of an Elevator Security System},
  series   = {Designs},
  volume    = {4},
  number    = {2},
  publisher = {MDPI},
  issn      = {2411-9660},
  doi       = {10.3390/designs4020009},
  url       = {https://nbn-resolving.org/urn:nbn:de:hbz:832-epub4-15248},
  year      = {2020},
  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 1.083s to an overall signal travel time of 1.338 s.},
  language  = {en}
}