A new automatic bearing fault size diagnosis using time–frequency images of CWT and deep transfer learning methods

KUNCAN, Fatma; KAYA, YILMAZ; Ertunc, Huseyin Metin

doi:10.55730/1300-0632.3909

A new automatic bearing fault size diagnosis using time–frequency images of CWT and deep transfer learning methods

Yılmaz KAYA, (Siirt Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü, Siirt, Türkiye)

Fatma KUNCAN, (Siirt Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü, Siirt, Türkiye)

Hüseyin Metin ERTUNÇ (Kocaeli Üniversitesi, Mühendislik Fakültesi, Mekatronik Mühendisliği Bölümü, Kocaeli, Türkiye)

Turkish Journal of Electrical Engineering and Computer Sciences

14 0

Yıl: 2022 Cilt: 30 Sayı: 5 Sayfa Aralığı: 1851 - 1867 Metin Dili: İngilizce DOI: 10.55730/1300-0632.3909 İndeks Tarihi: 08-12-2022

A new automatic bearing fault size diagnosis using time–frequency images of CWT and deep transfer learning methods

Öz:

Bearings are generally used as bearings or turning elements. Bearings are subjected to high loads and rapid speeds. Furthermore, metal-to-metal contact within the bearing makes it sensitive. In today’s machines, bearing failures disrupt the operation of the system or completely stop the system. Bearing failures that can occur can cause enormous damage to the entire system. Therefore, it is necessary to anticipate bearing failures and to carry out a regular diagnostic examination. Various systems have been developed for fault diagnosis. In recent years, deep transfer learning (DTL) methods are often preferred in current bearing diagnosis models, as they provide time savings and high success rates. Deep transfer learning models also improve diagnosis accuracy under certain conditions by greatly reducing human intervention. Diagnosis at the right time is very important for the sustainability and eﬀiciency of industrial production. A technique based on continuous wavelet transform (CWT) and two dimensional (2D) convolutional neural networks (CNN) is presented in this paper to detect fault size from vibration data of various bearing failure types. Time-frequency (TF) color scalogram images for bearing vibration signals were obtained using the CWT method. Using AlexNet, GoogleNet, Resnet, VGG16, and VGG19 deep transfer learning methods with scalogram images, fault size prediction from vibration signals was performed. Five different transfer deep learning models were used for three different data sets. It was observed that the success rates obtained varied between 96.67% and 100%.

Anahtar Kelime: Bearing faults fault detection fault classification continuous wavelet transform convolutional neural networks transfer learning

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık

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APA	KAYA Y, KUNCAN F, Ertunc H (2022). A new automatic bearing fault size diagnosis using time–frequency images of CWT and deep transfer learning methods. , 1851 - 1867. 10.55730/1300-0632.3909
Chicago	KAYA YILMAZ,KUNCAN Fatma,Ertunc Huseyin Metin A new automatic bearing fault size diagnosis using time–frequency images of CWT and deep transfer learning methods. (2022): 1851 - 1867. 10.55730/1300-0632.3909
MLA	KAYA YILMAZ,KUNCAN Fatma,Ertunc Huseyin Metin A new automatic bearing fault size diagnosis using time–frequency images of CWT and deep transfer learning methods. , 2022, ss.1851 - 1867. 10.55730/1300-0632.3909
AMA	KAYA Y,KUNCAN F,Ertunc H A new automatic bearing fault size diagnosis using time–frequency images of CWT and deep transfer learning methods. . 2022; 1851 - 1867. 10.55730/1300-0632.3909
Vancouver	KAYA Y,KUNCAN F,Ertunc H A new automatic bearing fault size diagnosis using time–frequency images of CWT and deep transfer learning methods. . 2022; 1851 - 1867. 10.55730/1300-0632.3909
IEEE	KAYA Y,KUNCAN F,Ertunc H "A new automatic bearing fault size diagnosis using time–frequency images of CWT and deep transfer learning methods." , ss.1851 - 1867, 2022. 10.55730/1300-0632.3909
ISNAD	KAYA, YILMAZ vd. "A new automatic bearing fault size diagnosis using time–frequency images of CWT and deep transfer learning methods". (2022), 1851-1867. https://doi.org/10.55730/1300-0632.3909

APA	KAYA Y, KUNCAN F, Ertunc H (2022). A new automatic bearing fault size diagnosis using time–frequency images of CWT and deep transfer learning methods. Turkish Journal of Electrical Engineering and Computer Sciences, 30(5), 1851 - 1867. 10.55730/1300-0632.3909
Chicago	KAYA YILMAZ,KUNCAN Fatma,Ertunc Huseyin Metin A new automatic bearing fault size diagnosis using time–frequency images of CWT and deep transfer learning methods. Turkish Journal of Electrical Engineering and Computer Sciences 30, no.5 (2022): 1851 - 1867. 10.55730/1300-0632.3909
MLA	KAYA YILMAZ,KUNCAN Fatma,Ertunc Huseyin Metin A new automatic bearing fault size diagnosis using time–frequency images of CWT and deep transfer learning methods. Turkish Journal of Electrical Engineering and Computer Sciences, vol.30, no.5, 2022, ss.1851 - 1867. 10.55730/1300-0632.3909
AMA	KAYA Y,KUNCAN F,Ertunc H A new automatic bearing fault size diagnosis using time–frequency images of CWT and deep transfer learning methods. Turkish Journal of Electrical Engineering and Computer Sciences. 2022; 30(5): 1851 - 1867. 10.55730/1300-0632.3909
Vancouver	KAYA Y,KUNCAN F,Ertunc H A new automatic bearing fault size diagnosis using time–frequency images of CWT and deep transfer learning methods. Turkish Journal of Electrical Engineering and Computer Sciences. 2022; 30(5): 1851 - 1867. 10.55730/1300-0632.3909
IEEE	KAYA Y,KUNCAN F,Ertunc H "A new automatic bearing fault size diagnosis using time–frequency images of CWT and deep transfer learning methods." Turkish Journal of Electrical Engineering and Computer Sciences, 30, ss.1851 - 1867, 2022. 10.55730/1300-0632.3909
ISNAD	KAYA, YILMAZ vd. "A new automatic bearing fault size diagnosis using time–frequency images of CWT and deep transfer learning methods". Turkish Journal of Electrical Engineering and Computer Sciences 30/5 (2022), 1851-1867. https://doi.org/10.55730/1300-0632.3909