Research on Equipment Failure Prediction Based on Machine Learning Models

Dong Yu

2025

Abstract

In the field of industrial production, the failure prediction of instruments is very important. The defect of the device effectively prevents problems such as the stay of the product caused by the failure and the decline in efficiency, to improve the stability of the product. On the other hand, broken equipment can eliminate potential accident risk, reduce maintenance costs, and prevent product expansion from being maintained. This article summarizes several new ideas for error prediction of devices, including deep learning-based techniques. The Bible learns from massive data and conducts error prediction through a deep learning model, comparing the predicted moral values with the true moral values. As such, it can accurately predict errors and monitor the device in real time using electronics on the Internet. After collecting the data, we conduct data analysis through various websites to obtain the predicted results. In addition, the interpretation methods of multitime crush data are reviewed to make error prediction. Using the decision tree, the relationship between the theme and the result is verified. This article explains in detail the content of each method and the specific applications or benefits of various techniques in industrial production.

Paper Citation

in Harvard Style

Yu D. (2025). Research on Equipment Failure Prediction Based on Machine Learning Models. In Proceedings of the 2nd International Conference on Innovations in Applied Mathematics, Physics, and Astronomy - Volume 1: IAMPA; ISBN 978-989-758-774-0, SciTePress, pages 199-204. DOI: 10.5220/0013822000004708

in Bibtex Style

@conference{iampa25,
author={Dong Yu},
title={Research on Equipment Failure Prediction Based on Machine Learning Models},
booktitle={Proceedings of the 2nd International Conference on Innovations in Applied Mathematics, Physics, and Astronomy - Volume 1: IAMPA},
year={2025},
pages={199-204},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013822000004708},
isbn={978-989-758-774-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Innovations in Applied Mathematics, Physics, and Astronomy - Volume 1: IAMPA
TI - Research on Equipment Failure Prediction Based on Machine Learning Models
SN - 978-989-758-774-0
AU - Yu D.
PY - 2025
SP - 199
EP - 204
DO - 10.5220/0013822000004708
PB - SciTePress