Wear particle image analysis

feature extraction, selection and classification by deep and machine learning

verfasst von

Joseph Vivek, Naveen Venkatesh S, Tapan K. Mahanta, Sugumaran V, M. Amarnath, Sangharatna M. Ramteke, Max Marian

Abstract

Purpose: This study aims to explore the integration of machine learning (ML) in tribology to optimize lubrication interval decisions, aiming to enhance equipment lifespan and operational efficiency through wear image analysis. Design/methodology/approach: Using a data set of scanning electron microscopy images from an internal combustion engine, the authors used AlexNet as the feature extraction algorithm and the J48 decision tree algorithm for feature selection and compared 15 ML classifiers from the lazy-, Bayes and tree-based families. Findings: From the analyzed ML classifiers, instance-based k-nearest neighbor emerged as the optimal algorithm with a 95% classification accuracy against testing data. This surpassed individually trained convolutional neural networks’ (CNNs) and closely approached ensemble deep learning (DL) techniques’ accuracy. Originality/value: The proposed approach simplifies the process, enhances efficiency and improves interpretability compared to more complex CNNs and ensemble DL techniques.

Organisationseinheit(en)

Institut für Maschinenkonstruktion und Tribologie

Externe Organisation(en)

Vellore Institute of Technology Chennai (VIT Chennai)
Lulea University of Technology
Pontificia Universidad Catolica de Chile
Indian Institute of Information Technology Design & Manufacturing Kancheepuram

Typ

Artikel

Journal

Industrial Lubrication and Tribology

Band

76

Seiten

599-607

Anzahl der Seiten

9

ISSN

0036-8792

Publikationsdatum

26.06.2024

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Maschinenbau, Energie (insg.), Oberflächen, Beschichtungen und Folien

Elektronische Version(en)

https://doi.org/10.1108/ILT-12-2023-0414 (Zugang: Geschlossen)