Version 1
: Received: 18 July 2024 / Approved: 18 July 2024 / Online: 19 July 2024 (02:37:19 CEST)
How to cite:
Pujatti, M.; Calzà, D.; Gobbi, A.; Svaizer, P.; Cristoforetti, M. MoPiDiP: A Modular Real-Time Pipeline for Machinery Diagnosis and Prognosis Based on Deep Learning Algorithms. Preprints2024, 2024071522. https://doi.org/10.20944/preprints202407.1522.v1
Pujatti, M.; Calzà, D.; Gobbi, A.; Svaizer, P.; Cristoforetti, M. MoPiDiP: A Modular Real-Time Pipeline for Machinery Diagnosis and Prognosis Based on Deep Learning Algorithms. Preprints 2024, 2024071522. https://doi.org/10.20944/preprints202407.1522.v1
Pujatti, M.; Calzà, D.; Gobbi, A.; Svaizer, P.; Cristoforetti, M. MoPiDiP: A Modular Real-Time Pipeline for Machinery Diagnosis and Prognosis Based on Deep Learning Algorithms. Preprints2024, 2024071522. https://doi.org/10.20944/preprints202407.1522.v1
APA Style
Pujatti, M., Calzà, D., Gobbi, A., Svaizer, P., & Cristoforetti, M. (2024). MoPiDiP: A Modular Real-Time Pipeline for Machinery Diagnosis and Prognosis Based on Deep Learning Algorithms. Preprints. https://doi.org/10.20944/preprints202407.1522.v1
Chicago/Turabian Style
Pujatti, M., Piergiorgio Svaizer and Marco Cristoforetti. 2024 "MoPiDiP: A Modular Real-Time Pipeline for Machinery Diagnosis and Prognosis Based on Deep Learning Algorithms" Preprints. https://doi.org/10.20944/preprints202407.1522.v1
Abstract
Condition monitoring is a crucial process for ensuring industrial assets' reliability and operational efficiency. In the age of the digital industry, 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 using MQTT, 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.
Keywords
data pipeline; predictive maintenance; condition monitoring; domain adaptation; fault diagnosis; remaining useful life estimation
Subject
Computer Science and Mathematics, Data Structures, Algorithms and Complexity
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
