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Supachate Innet. An investigation of machine learning algorithms for predictive maintenance in high pressure processing systems. (). King Mongkut's University of Technology North Bangkok. Central Library. : , 2024.
An investigation of machine learning algorithms for predictive maintenance in high pressure processing systems
Name: Suradach Srisuwan
Organization :
University of the Thai Chamber of Commerce. Department of Computer Engineering and Artificial Intelligence
Email :
suradach84@hotmail.com
Name: Supachate Innet
Organization :
University of the Thai Chamber of Commerce. Department of Computer Engineering and Artificial Intelligence
Email :
supachate_inn@utcc.ac.th
keyword:
Predictive maintenance
LCSH:
Machine learning
LCSH:
Big data
Abstract:
In this research proposal to enhance the
operational efficiency and prolong the usable life of industrial
machinery and equipment through predictive maintenance
leveraging machine-learning techniques. As manufacturers
increasingly prioritize cost reduction, minimized downtime,
and enhanced operational uptime, the adoption of proactive
maintenance strategies becomes imperative. This study intends
to gather historical datasets to train machine-learning models
for predicting equipment failures and develop an algorithmic
framework for proactive maintenance scheduling. The primary
objective is to contribute to the development of an efficient
predictive maintenance model, thereby reducing industrial
maintenance costs and positively impacting product costs.This
research will employ various machine-learning approaches, big
data preprocessing techniques, and feature engineering
methodologies. Data preprocessing will involve cleaning,
conversion, and standardization of datasets before model
training. Feature engineering will focus on selecting the most
relevant features for accurate machine failure prediction.
Multiple machine-learning algorithms, including Random
Forest (RF), Support Vector Machines (SVM), and Long
Short-Term Memory (LSTM), will be evaluated to determine
the most effective model for precise predictions. The
comparative predictive performance utilizing by Root Mean
Square Error (RMSE), R-squared (R2), Mean Absolute Error
(MAE) to measure performance metric. The best performing
machine learning models in this study have been deployed into
real operation in factory. The best model expect to archive
successful result by 5-10% increase operation efficiency
King Mongkut's University of Technology North Bangkok. Central Library
Address:
BANGKOK
Email:
library@kmutnb.ac.th
Created:
2024
Modified:
2025-05-21
Issued:
2025-05-21
บทความ/Article
application/pdf
BibliograpyCitation :
In IEEE Thailand Section (IEEE Computer Society Thailand Chapter) and Prince of Songkla University. College of Computing. The 21st International Joint Conference on Computer Science and Software Engineering (JCSSE 2024)) (pp.94-98). Phuket : Prince of Songkla University
eng
©copyrights King Mongkut's University of Technology North Bangkok
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Suradach Srisuwan
| Title | Contributor | Type |
|---|---|---|
| An investigation of machine learning algorithms for predictive maintenance in high pressure processing systems
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Suradach Srisuwan;Supachate Innet | บทความ/Article |
Supachate Innet
| Title | Contributor | Type |
|---|---|---|
| An investigation of machine learning algorithms for predictive maintenance in high pressure processing systems
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Suradach Srisuwan;Supachate Innet | บทความ/Article | |
| An experimental study on ultrasonic clothes drying system
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Supachate Innet;Supanunt Tunwannarux | บทความ/Article |