Back to articles
ACDM 2024 Digital Media Special Issue
Volume: 69 | Article ID: 010404
Image
Big-Data-driven Part Anomaly Detection under Smart Manufacturing: A Study based on GAM–Boost Technology
  DOI :  10.2352/J.ImagingSci.Technol.2025.69.1.010404  Published OnlineJanuary 2025
Abstract
Abstract

In the smart manufacturing process, it is important to closely monitor manufactured parts. To solve the problem of part anomaly detection, this paper proposes a GAM–Boost anomaly detection model using a large-scale dataset (14.3 GB) from the Kaggle competition “Bosch Production Line Performance.” The model first selects the important features using the XGBoost algorithm and then captures the nonlinear relationships between the features using the generalized additive model. To capture the nonlinear relationships between features and at the same time improve the model’s ability to understand the data relationships, feature engineering techniques are applied to transform the nonlinear relationships without ignoring the linear relationship features. Finally the XGBoost model is optimized for anomaly detection using the Bayesian algorithm. The experimental results show that the model achieves lower errors on both training and test sets, the generalization performance of the model is significantly improved, it can better adapt to various data situations, and it achieves better results in terms of flexibility and prediction accuracy.

Subject Areas :
Views 27
Downloads 12
 articleview.views 27
 articleview.downloads 12
  Cite this article 

Lifeng Lu, Zhanjun Si, ZhanShuo Liu, TongDa Mei, "Big-Data-driven Part Anomaly Detection under Smart Manufacturing: A Study based on GAM–Boost Technologyin Journal of Imaging Science and Technology,  2025,  pp 1 - 9,  https://doi.org/10.2352/J.ImagingSci.Technol.2025.69.1.010404

 Copy citation
  Copyright statement 
Copyright © Society for Imaging Science and Technology 2025
  Article timeline 
  • received May 2024
  • accepted September 2024
  • PublishedJanuary 2025

Preprint submitted to:
  Login or subscribe to view the content