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Mechanical Systems

Mechanical Systems

Mechanical systems are analogous to to electrical networks where springs and masses play similar roles to inductors and capacitors, respectively. A viscous damper in mechanical systems functions similarly to a resistor in electrical networks, dissipating energy. The forces acting on a mass in such systems include an applied force in the direction of motion, counteracted by forces from the spring, a viscous damper, and the mass's acceleration. This interplay of forces is mathematically...

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A Modular Microfluidic Technology for Systematic Studies of Colloidal Semiconductor Nanocrystals

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A Comprehensive Framework for the Modular Development of Condition Monitoring Systems for a Continuous Dry

Rexonni B Lagare¹, M Ziyan Sheriff¹, Marcial Gonzalez²

¹Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47907, USA.

International Symposium on Process Systems Engineering

|February 15, 2023

Summary

This summary is machine-generated.

This study presents a framework for developing robust modular condition monitoring systems. It integrates modules, handles prediction uncertainty, and ensures quality assurance for reliable operations.

Keywords:

Bayesian Condition Monitoring Machine Learning Modular Probabilistic Programming

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Area of Science:

Engineering
Computer Science

Background:

Condition monitoring systems (CMS) are often developed modularly, with components embedded or distributed.
Integrating these diverse modules presents challenges in managing system-wide performance and reliability.

Purpose of the Study:

To introduce a framework for the modular development and integration of CMS.
To address the uncertainty in predictions from constituent monitoring modules.
To enhance the robustness and quality assurance of modular CMS.

Main Methods:

Development of an integration framework for modular CMS.
Implementation of a comprehensive model to manage prediction uncertainty.
Methodology for quality assurance and handling module downtime.

Main Results:

A structured approach to modular CMS development.
Improved handling of uncertainty in CMS predictions.
Enhanced system robustness and reduced unnecessary shutdowns.

Conclusions:

The proposed framework effectively guides the modular development and integration of CMS.
The framework enhances system reliability by managing uncertainty and ensuring quality assurance.
This approach supports continuous operation and prevents disruptions from module maintenance.