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A Framework for the Optimization of Complex Cyber-Physical Systems via Directed Acyclic Graph.

Manuel Castejón-Limas1, Laura Fernández-Robles1, Héctor Alaiz-Moretón1

  • 1Department of Mechanical, Computer Science and Aerospace Engineering, Universidad de León, 24071 León, Spain.

Sensors (Basel, Switzerland)
|February 26, 2022
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Summary
This summary is machine-generated.

PipeGraph is an open-source Python library that extends scikit-learn Pipelines using Directed Acyclic Graphs (DAGs) for Cyber-Physical Systems (CPS). It simplifies machine learning model creation for industrial process optimization and anomaly detection.

Keywords:
Cyber-Physical SystemsDirected Acyclic GraphsLean Manufacturingmachine learning modelspipegraphscikit-learn

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Area of Science:

  • Computer Science
  • Machine Learning
  • Control Systems

Background:

  • Industrial process optimization in Cyber-Physical Systems (CPS) increasingly relies on mathematical modeling and data-driven methods.
  • Machine learning workflows often involve sequential data processing and model training steps.
  • Existing tools like scikit-learn's Pipeline offer sequential assembly but lack graph-based flexibility.

Purpose of the Study:

  • Introduce PipeGraph, an open-source Python toolbox designed to facilitate the creation of machine learning models for CPS.
  • Extend the functionality of scikit-learn's Pipeline by implementing a Directed Acyclic Graph (DAG) structure.
  • Enable more diverse operations beyond simple transformations within a graph framework for enhanced model building.

Main Methods:

  • Developed PipeGraph as a Python library compatible with scikit-learn, utilizing DAG structures.
  • Implemented features allowing diverse operations, access to intermediate data, and compatibility with hyperparameter tuning (GridSearchCV).
  • Validated PipeGraph through two case studies: optimizing a heat exchange management system and detecting manufacturing process anomalies.

Main Results:

  • PipeGraph successfully extends scikit-learn's Pipeline concept to DAGs, offering greater flexibility in machine learning model construction for CPS.
  • The library provides access to intermediate data and supports advanced operations, streamlining complex modeling tasks.
  • Case studies demonstrated PipeGraph's effectiveness in optimizing industrial systems and improving anomaly detection.

Conclusions:

  • PipeGraph is a valuable open-source tool for building and optimizing machine learning models in Cyber-Physical Systems.
  • Its DAG-based approach enhances flexibility and data accessibility compared to traditional sequential pipelines.
  • The library is well-documented, tested, and integrated within the scikit-learn ecosystem, promoting wider adoption.