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A condition monitoring methodology using deep-learning-based surrogate models and parameter identification applied to heat pumps

dataset
posted on 2025-02-04, 12:43 authored by Pieter Gerhardus RousseauPieter Gerhardus Rousseau, Ryno Laubscher

The datasets accompany the paper titled "A condition monitoring methodology using deep-learning-based surrogate models and parameter identification applied to heat pumps".

The datasets were generated via a custom-developed physics-based heat pump model that includes degradation factors on specific components. The dataset was used for training deep-learning-based surrogate models and to demonstrate a condition monitoring methodology using parameter identification.

History

Publisher

Stellenbosch University

Contributor

Rousseau, PG; & Laubscher, R.

Date

2024-06-13

Format

.xlsx

Language

en

Geographical Location

South Africa

Academic Group

  • Engineering

Recommended Citation

Rousseau, PG & Laubscher, R. 2024. A condition monitoring methodology using deep-learning-based surrogate models and parameter identification applied to heat pumps. Stellenbosch University. Dataset. DOI: https://doi.org/10.25413/sun.26027368

Sustainable Development Goals (SDGs)

  • Goal 9​: INDUSTRY, INNOVATION & INFRASTRUCTURE

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