Fault detection of CNC machines from vibration signals using machine learning methods
Küçük Resim Yok
Tarih
2020
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Every machine used in industry has become indispensable in today’s production process. As a result, malfunctions of these machines have devastating consequences. In this context, it is very important to diagnose faults before or during the start-up phase. In this paper, the vibration signals obtained from the computerized CNC machine drill bit whiledrilling the metal plate. For fault diagnosis, feature extraction was first performed. A total of 21 properties were extracted from the vibration data in the time domain, frequency domain, and time-frequency domain. The features that were taken at certain intervals from the dataset were doubled to get more accurate results. After the feature extraction process, the data was normalized. The normalization process was determined as min-max and z-score. After the normalization process, classification was carried out by Support Vector Machine (SVM) and K-nearest neighbors (kNN) methods. As a result of these processes, %96,45 and %94,37 accuracy rates for SVM and kNN were obtained respectively.
Açıklama
Anahtar Kelimeler
CNC Drill Bit Faults, Fault Diagnosis, KNN, Machine Learning, SVM
Kaynak
Lecture Notes on Data Engineering and Communications Technologies
WoS Q Değeri
N/A
Scopus Q Değeri
Q3
Cilt
43
