Data-Driven Predictive Maintenance: A Methodology Primer

Cerquitelli, Tania; Nikolakis, Nikolaos; Morra, Lia; Bellagarda, Andrea; Orlando, Matteo; Salokangas, Riku; Saarela, Olli; Hietala, Jani; Kaarmila, Petri; Macii, Enrico

doi:10.1007/978-981-16-2940-2_3

Cited by 5 publications

(2 citation statements)

References 52 publications

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“…As data is at the centre of the PdM system, defining a data management system to handle it for the various activities inside the PdM framework greatly matters. Cerquitelli et al (2021) discuss this data management system as 'services' to ease the implementation of predictive maintenance. Numerous software are used to define the system and the different steps linked with the activities of PdM.…”

Section: Literature Reviewmentioning

confidence: 99%

Development of a flexible data management system, to implement predictive maintenance in the Industry 4.0 context

Ciancio

Homri

Dantan

et al. 2023

International Journal of Production Research

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In recent years, the way that maintenance is carried out has evolved due to the incorporation of digital tools and Industry 4.0 concepts. By connecting to and communicating with their production system, companies can now gather information about the current and future health of the equipment, enabling more efficient control through a process called predictive maintenance (PdM). The goal of PdM is to reduce unplanned downtimes and proactively address maintenance needs before failures occur. However, it can be challenging for industrial practitioners to implement an intelligent maintenance system that effectively manages data. This paper presents a methodology for developing and implementing a PdM system in the automotive industry, using open standards and scalable data management capabilities. The platform is validated through the presentation of two industry use cases.

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Section: Literature Reviewmentioning

confidence: 99%

Development of a flexible data management system, to implement predictive maintenance in the Industry 4.0 context

Ciancio

Homri

Dantan

et al. 2023

International Journal of Production Research

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show abstract

“…Many academic research works and industrial R&D projects are focused on diagnostics and predictive maintenance (Prognostics and health management, PHM) [1] [2]. Also, several leading scientific journals are specifically dedicated to this research field involving many researchers and engineers from different disciplines: general computing, computer networks and IoT (Internet of Things), advanced statistics, signal processing, Artificial Intelligence, data science and data mining, intensive computing, embedded systems, etc..…”

Section: Introductionmentioning

confidence: 99%

Machine Learning based Real Time Predictive Maintenance at the Edge for Manufacturing Systems: A Practical Example

Ringler,

Knittel,

Ponsart

et al. 2023

2023 IEEE IAS Global Conference on Emerging Technologies (GlobConET)

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Edge Computing is becoming more and more essential for the Industrial Internet of Things (industrial IoT) for predictive maintenance in the industry 4.0 framework. The transition from central to distributed approaches (edge nodes), will enhance the capabilities of handling real-time big data from IoT by ensuring low latency and high bandwidth. Moreover, with the developed architecture the possibility is given to have distributed Machine Learning (ML) by enabling edge devices to learn local ML models. Therefore, the performances of the global diagnostic models can be improved. In this paper, the developed setup is composed of PC's, NVIDIA Jetson Nano Developers kits (for edge computing), and a smartphone for real-time displaying. The implemented real-time supervised machine learning approaches are applied on an industrial oilinjection screw compressor instrumented with vibration sensors. Time domain features are calculated online with the help of sliding windows and the features are automatically classified. Embedded in the equipment, the used algorithms obtained very good real-time diagnostic performances.

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Detection of Novel Asset Failures in Predictive Maintenance Using Classifier Certainty

Fathi,

Ristin,

Sadurski

et al. 2024

2024 32nd Mediterranean Conference on Control and Automation (MED)

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Data-Driven Predictive Maintenance: A Methodology Primer

Cited by 5 publications

References 52 publications

Development of a flexible data management system, to implement predictive maintenance in the Industry 4.0 context

Development of a flexible data management system, to implement predictive maintenance in the Industry 4.0 context

Machine Learning based Real Time Predictive Maintenance at the Edge for Manufacturing Systems: A Practical Example

Detection of Novel Asset Failures in Predictive Maintenance Using Classifier Certainty

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