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Milling cutter fault diagnosis using unsupervised learning on small data: A robust and autonomous framework

Eksploatacja i Niezawodnosc · Ocak 2024

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YÖKSİS Kayıtları
Milling cutter fault diagnosis using unsupervised learning on small data: A robust and autonomous framework
Eksploatacja i Niezawodność – Maintenance and Reliability · 2024 SCI
DOÇENT MUSTAFA KUNTOĞLU →

Makale Bilgileri

DergiEksploatacja i Niezawodnosc
Yayın TarihiOcak 2024
Cilt / Sayfa26
DOI10.17531/ein/178274
Scopus ID2-s2.0-85190993985
Erişim🔓 Açık Erişim
Özet Tool condition affects the tolerances and the energy consumption and hence needs to be monitored. Artificial intelligence (AI) based data-driven techniques for tool condition determination are proposed. Unfortunately, the data-driven techniques are data-hungry. This paper proposes a methodology for classification based on unsupervised learning using limited unlabeled training data. The work presents a multi-class classification problem for the tool condition monitoring. The principal component analysis (PCA) is employed for dimensionality reduction and the principal components (PCs) are used as input for classification using k-means clustering. New collected data is then projected on the PC space, and classified using the clusters from the training. The methodology has been applied for classification of tool faults in 6 classes in a vertical milling center. The use of limited input parameters from the user makes the method ideal for monitoring a large number of machines with minimal human intervention. Furthermore, due to the small amount of data needed for the training, the method has the potential to be transferable.

Yazarlar (5)

1
Abhishek Dhananjay Patange
ORCID: 0000-0002-9130-694X
2
Rohan Soman
ORCID: 0000-0002-5499-2565
3
Sujit S. Pardeshi
4
Mustafa Kuntoğlu
ORCID: 0000-0002-7291-9468
5
Wieslaw Ostachowicz
ORCID: 0000-0002-8061-8614

Anahtar Kelimeler

k-means clustering milling cutter PCA tool condition monitoring (TCM)

Kurumlar

COEP Technological University
India
Polskiej Akademii Nauk, Instytut Maszyn Przepływowych im. Roberta Szewalskiego
Gdansk Poland
Selçuk Üniversitesi
Selçuklu Turkey

Metrikler

5
Atıf
5
Yazar
4
Anahtar Kelime

Sistemimizdeki Yazarlar

MK
MUSTAFA KUNTOĞLU
DOÇENT

Hızlı Erişim

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