Abstract
Mathematical modeling and data-driven methodologies are frequently required to optimize industrial processes in the context of Cyber-Physical Systems (CPS). This paper introduces the PipeGraph software library, an open-source python toolbox for easing the creation of machine learning models by using Directed Acyclic Graph (DAG)-like implementations that can be used for CPS. scikit-learn’s Pipeline is a very useful tool to bind a sequence of transformers and a final estimator in a single unit capable of working itself as an estimator. It sequentially assembles several steps that can be cross-validated together while setting different parameters. Steps encapsulation secures the experiment from data leakage during the training phase. The scientific goal of PipeGraph is to extend the concept of Pipeline by using a graph structure that can handle scikit-learn’s objects in DAG layouts. It allows performing diverse operations, instead of only transformations, following the topological ordering of the steps in the graph; it provides access to all the data generated along the intermediate steps; and it is compatible with GridSearchCV function to tune the hyperparameters of the steps. It is also not limited to entries. Moreover, it has been proposed as part of the scikit-learn-contrib supported project, and is fully compatible with scikit-learn. Documentation and unitary tests are publicly available together with the source code. Two case studies are analyzed in which PipeGraph proves to be essential in improving CPS modeling and optimization: the first is about the optimization of a heat exchange management system, and the second deals with the detection of anomalies in manufacturing processes.
Highlights
Publisher’s Note: MDPI stays neutralContinuous technological advancements in fields such as Information Technology (IT), Artificial Intelligence (AI), and the Internet of Things (IoT), among others, have drastically transformed manufacturing processes
Cyber–Physical Systems (CPS) and Lean Manufacturing (LM) can greatly leverage on improvements in the tools and techniques available for system modeling
Data leakage, being one of the most common sources of unexpected behavior when the fitted models are stressed with actual demands, can largely be prevented by using encapsulation techniques such as the Pipeline provided by the scikit-learn library
Summary
Publisher’s Note: MDPI stays neutralContinuous technological advancements in fields such as Information Technology (IT), Artificial Intelligence (AI), and the Internet of Things (IoT), among others, have drastically transformed manufacturing processes. CPS has considerably improved the efficiency of production processes while making them more resilient and collaborative [1]. These cutting-edge technologies are advancing the manufacturing economic sector in the Industry 4.0 era [2]. In the Industry 4.0 paradigm, manufacturing industries must modify their management systems and look for new manufacturing strategies [3,4] to find solutions to tackle the issues faced nowadays. Lean Manufacturing (LM) has become one of the most generally accepted manufacturing methods and management styles used by organizations throughout the world to improve their business performance and competitiveness [5]. Since LM improves operational performance for manufacturing organizations in developing and with regard to jurisdictional claims in published maps and institutional affiliations
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