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<p>Condition monitoring is a crucial process for ensuring industrial assets’ reliability and operational efficiency. In the age of the digital industry, Artificial Intelligence (AI)-based data-driven condition monitoring is proving extremely effective in detecting potential issues before they escalate into major problems, thereby reducing downtime, minimizing maintenance costs, and extending the lifespan of the equipment. The availability of tools that can enable the operationalization of these data-driven solutions is, therefore, critical. In this direction, this work proposes a comprehensive, modular, and scalable pipeline covering all the steps from the data acquisition to the AI model training and inference phases. The tool integrates the data acquisition and processing steps with a configurable feature extraction phase. Moreover, the system also integrates deep learning algorithms for diagnosis and prognosis, including a domain adaptation stage to permit transfer learning and increase generalizability. In addition, it features a communication system which allows for an online data stream to enable real-time monitoring and maintenance. The overall infrastructure was deployed in actual industrial settings and tested in a real-time experiment, demonstrating the proposed approach’s validity.</p>

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Mopidip: a modular real-time pipeline for machinery diagnosis and prognosis based on deep learning algorithms

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月4日

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  • Mattia Pujatti,
  • Davide Calzà,
  • Andrea Gobbi,
  • Piergiorgio Svaizer,
  • Marco Cristoforetti

摘要

Condition monitoring is a crucial process for ensuring industrial assets’ reliability and operational efficiency. In the age of the digital industry, Artificial Intelligence (AI)-based data-driven condition monitoring is proving extremely effective in detecting potential issues before they escalate into major problems, thereby reducing downtime, minimizing maintenance costs, and extending the lifespan of the equipment. The availability of tools that can enable the operationalization of these data-driven solutions is, therefore, critical. In this direction, this work proposes a comprehensive, modular, and scalable pipeline covering all the steps from the data acquisition to the AI model training and inference phases. The tool integrates the data acquisition and processing steps with a configurable feature extraction phase. Moreover, the system also integrates deep learning algorithms for diagnosis and prognosis, including a domain adaptation stage to permit transfer learning and increase generalizability. In addition, it features a communication system which allows for an online data stream to enable real-time monitoring and maintenance. The overall infrastructure was deployed in actual industrial settings and tested in a real-time experiment, demonstrating the proposed approach’s validity.