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Digital Twins & ZeroConf AI: Structuring Automated Intelligent Pipelines for Industrial Applications

Marco Picone, Fabio Turazza, Matteo Martinelli, Marco Mamei

TL;DR

This paper addresses the fragmentation of CPS IoT/IIoT ecosystems that impede scalable AI deployment. It proposes a modular Digital Twin architecture and a ZeroConf AI pipeline that decouples data management from AI components. Key contributions include characterizing DT capabilities (Representativeness, Memorization, Augmentation, Replication), defining a ZeroConf pipeline framework, and validating it in a MicroFactory with accelerometer data. The approach promises faster deployment of intelligent services in complex industrial environments by reducing configuration effort and enabling concurrent ML experimentation.

Abstract

The increasing complexity of Cyber-Physical Systems (CPS), particularly in the industrial domain, has amplified the challenges associated with the effective integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques. Fragmentation across IoT and IIoT technologies, manifested through diverse communication protocols, data formats and device capabilities, creates a substantial gap between low-level physical layers and high-level intelligent functionalities. Recently, Digital Twin (DT) technology has emerged as a promising solution, offering structured, interoperable and semantically rich digital representations of physical assets. Current approaches are often siloed and tightly coupled, limiting scalability and reuse of AI functionalities. This work proposes a modular and interoperable solution that enables seamless AI pipeline integration into CPS by minimizing configuration and decoupling the roles of DTs and AI components. We introduce the concept of Zero Configuration (ZeroConf) AI pipelines, where DTs orchestrate data management and intelligent augmentation. The approach is demonstrated in a MicroFactory scenario, showing support for concurrent ML models and dynamic data processing, effectively accelerating the deployment of intelligent services in complex industrial settings.

Digital Twins & ZeroConf AI: Structuring Automated Intelligent Pipelines for Industrial Applications

TL;DR

This paper addresses the fragmentation of CPS IoT/IIoT ecosystems that impede scalable AI deployment. It proposes a modular Digital Twin architecture and a ZeroConf AI pipeline that decouples data management from AI components. Key contributions include characterizing DT capabilities (Representativeness, Memorization, Augmentation, Replication), defining a ZeroConf pipeline framework, and validating it in a MicroFactory with accelerometer data. The approach promises faster deployment of intelligent services in complex industrial environments by reducing configuration effort and enabling concurrent ML experimentation.

Abstract

The increasing complexity of Cyber-Physical Systems (CPS), particularly in the industrial domain, has amplified the challenges associated with the effective integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques. Fragmentation across IoT and IIoT technologies, manifested through diverse communication protocols, data formats and device capabilities, creates a substantial gap between low-level physical layers and high-level intelligent functionalities. Recently, Digital Twin (DT) technology has emerged as a promising solution, offering structured, interoperable and semantically rich digital representations of physical assets. Current approaches are often siloed and tightly coupled, limiting scalability and reuse of AI functionalities. This work proposes a modular and interoperable solution that enables seamless AI pipeline integration into CPS by minimizing configuration and decoupling the roles of DTs and AI components. We introduce the concept of Zero Configuration (ZeroConf) AI pipelines, where DTs orchestrate data management and intelligent augmentation. The approach is demonstrated in a MicroFactory scenario, showing support for concurrent ML models and dynamic data processing, effectively accelerating the deployment of intelligent services in complex industrial settings.
Paper Structure (12 sections, 5 figures, 2 tables)

This paper contains 12 sections, 5 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. DT Capabilities & ZeroConf Intelligence
  4. Representativeness & Contextualization
  5. Memorization
  6. Augmentation
  7. Replication
  8. DT Blueprint ZeroConf Architecture
  9. Experimental Phase Validation
  10. Experimental Setup
  11. Obtained Results
  12. Conclusions

Figures (5)

  • Figure 1: Schematic representation of how core DT capabilities interconnect and support AI functionalities within a DT instance.
  • Figure 2: Blueprint architecture of the ZeroConf approach with the three main layers characterizing a DT instance.
  • Figure 3: Experimental Microfactory with the different machines, communication protocols, sensors and DTs.
  • Figure 4: The top three charts show the raw accelerometer signals recorded along the X, Y and Z axes during our microfactory’s experimental phase. Directly beneath each axis plot, we present the corresponding Digital Twin preprocessing step: a rolling‐maximum extraction that condenses each signal into its block‐wise peak values, simplifying the downstream segmentation and clustering.
  • Figure 5: Top graph (Readiness): showing cleaned signal through DT. Middle graph (Replication): showing panels for penalties with color phases. Bottom graph (Augment): showing dashed change-points and colored segments, anomaly marked.