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Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
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Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
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Mathematical modeling transforms real-world scenarios into mathematical expressions, allowing for structured problem-solving and analysis. This process involves defining the situation, assigning variables to measurable quantities, selecting an appropriate model, and solving the resulting equation. Such models are invaluable in finance, providing precise methods to evaluate investments, loans, and repayment structures.A widely used example is the calculation of fixed monthly payments on a loan,...
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
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Updated: Dec 3, 2025

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
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A Model for Predictive Maintenance Based on Asset Administration Shell.

Salvatore Cavalieri1, Marco Giuseppe Salafia1

  • 1Department of Electrical Electronic and Computer Engineering, University of Catania, 95125 Catania, Italy.

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|October 29, 2020
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Summary

This study presents a novel, technology-independent model for predictive maintenance, crucial for Industry 4.0. It enhances machine upkeep by leveraging the RAMI 4.0 Asset Administration Shell for greater interoperability and flexibility.

Keywords:
CPSIIoTasset administration shelldigital twinindustry 4.0predictive maintenance

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Area of Science:

  • Industrial Engineering
  • Manufacturing Systems
  • Data Science

Background:

  • Predictive maintenance optimizes industrial upkeep by forecasting failures using historical data.
  • Industry 4.0 demands flexible, adaptable manufacturing for customization, challenging current maintenance solutions.
  • Vendor-specific and heterogeneous technologies hinder interoperability in Industry 4.0 predictive maintenance.

Purpose of the Study:

  • To introduce a generic, technology-independent model for predictive maintenance.
  • To enhance interoperability and flexibility in industrial maintenance solutions.
  • To align predictive maintenance strategies with Industry 4.0 principles.

Main Methods:

  • Development of a novel predictive maintenance model.
  • Leveraging the Reference Architecture Model for Industry (RAMI) 4.0.
  • Utilizing the Asset Administration Shell for device interoperability.

Main Results:

  • A generic and technology-independent model for predictive maintenance was defined.
  • The model facilitates interoperability between diverse industrial devices.
  • Generic functionalities for predictive maintenance were implemented.

Conclusions:

  • The proposed model enhances flexibility and interoperability for Industry 4.0.
  • It offers a standardized approach to predictive maintenance across heterogeneous systems.
  • This facilitates more adaptable and efficient industrial production environments.