Improving Temperature Estimation Models using Machine Learning Techniques
(2024)Department of Automatic Control
- Abstract
- Temperature estimation models are crucial for various products manufactured by BorgWarner. These models often require manual calibration, where experts adjust parameters to ensure accuracy. However, this process can be slow and prone to errors. This thesis investigates how Machine Learning techniques can be used to improve accuracy and efficiency of temperature estimation models.
Both black-box and grey-box approaches are used to evaluate the effectiveness of machine learning-based calibration. The black-box model employs techniques such as Decision Trees, Random Forests, and Neural Networks to predict temperature directly from raw input data, bypassing traditional temperature estimation processes. The grey-box model, on the other hand,... (More) - Temperature estimation models are crucial for various products manufactured by BorgWarner. These models often require manual calibration, where experts adjust parameters to ensure accuracy. However, this process can be slow and prone to errors. This thesis investigates how Machine Learning techniques can be used to improve accuracy and efficiency of temperature estimation models.
Both black-box and grey-box approaches are used to evaluate the effectiveness of machine learning-based calibration. The black-box model employs techniques such as Decision Trees, Random Forests, and Neural Networks to predict temperature directly from raw input data, bypassing traditional temperature estimation processes. The grey-box model, on the other hand, uses Deep Q-learning to adjust the calibration automatically.
Results show that the black box model achieves better performance compared to conventional temperature estimation methods. Meanwhile, the grey-box model not only significantly improves accuracy compared to the manual calibration method, but also reduces the need for manual calibration in temperature estimation models. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9164699
- author
- Dang, Van Duy and Elessawi, Basim
- supervisor
- organization
- year
- 2024
- type
- H3 - Professional qualifications (4 Years - )
- subject
- report number
- TFRT-6229
- other publication id
- 0280-5316
- language
- English
- id
- 9164699
- date added to LUP
- 2024-06-17 14:20:28
- date last changed
- 2024-06-17 14:20:28
@misc{9164699,
abstract = {{Temperature estimation models are crucial for various products manufactured by BorgWarner. These models often require manual calibration, where experts adjust parameters to ensure accuracy. However, this process can be slow and prone to errors. This thesis investigates how Machine Learning techniques can be used to improve accuracy and efficiency of temperature estimation models.
Both black-box and grey-box approaches are used to evaluate the effectiveness of machine learning-based calibration. The black-box model employs techniques such as Decision Trees, Random Forests, and Neural Networks to predict temperature directly from raw input data, bypassing traditional temperature estimation processes. The grey-box model, on the other hand, uses Deep Q-learning to adjust the calibration automatically.
Results show that the black box model achieves better performance compared to conventional temperature estimation methods. Meanwhile, the grey-box model not only significantly improves accuracy compared to the manual calibration method, but also reduces the need for manual calibration in temperature estimation models.}},
author = {{Dang, Van Duy and Elessawi, Basim}},
language = {{eng}},
note = {{Student Paper}},
title = {{Improving Temperature Estimation Models using Machine Learning Techniques}},
year = {{2024}},
}