A New One-Dimensional Convolutional Neural Network Model for Detecting Motor Bearing Failures

Year 2023, , 669 - 678, 01.09.2023

Merve Ertarğın , Özal Yıldırım , Ahmet Orhan

https://doi.org/10.35234/fumbd.1292390

Cited By: 2

Abstract

Electric motors have an important place in industry due to their ability to automate and facilitate various processes. Faults that occur in electric motors may affect the operation of the device or system and cause great financial losses. It is therefore critical to detect faults at an early stage. The use of computer-aided software in the detection of faults comes to the fore due to its cost and time saving potential. In this study, a deep learning-based model is proposed to detect engine bearing failure types. With this model, which uses one-dimensional convolutional neural network (1D-CNN) architecture, fault type detection is provided by using only vibration data. The proposed architecture is an efficient model that uses vibration signals to diagnose engine faults quickly and reliably. Within the scope of the study, detailed performance evaluations of the training and testing stages were provided by using different speed scenarios. With this model, which has a high generalization ability, fault detection has been made with high accuracy rates in different scenarios.

Keywords

Deep learning, convolutional neural network, motor fault detection

Motor Yataklarında Meydana Gelen Arızaları Tespit Etmek için Yeni Bir Tek Boyutlu Konvolüsyonel Sinir Ağı Modeli

Abstract

Elektrik motorları, çeşitli işlemleri otomatikleştirme ve kolaylaştırma yeteneklerinden dolayı endüstride önemli bir yere sahiptir. Elektrik motorlarında meydana gelen arızalar, cihazın veya sistemin çalışmasını etkileyebilmekte ve büyük maddi kayıplara neden olabilmektedir. Bu nedenle arızaların erken aşamada tespit edilmesi kritik bir öneme sahiptir. Arızaların tespitinde bilgisayar destekli yazılımlar kullanılması maliyetten ve zamandan tasarruf etme potansiyeli nedeniyle ön plana çıkmaktadır. Bu çalışmada, motor yatağı arıza türlerini tespit etmek için derin öğrenme tabanlı bir model önerilmiştir. Tek boyutlu konvolüsyonel sinir ağı (1D-CNN) mimarisi kullanan bu model ile sadece titreşim verileri kullanılarak arıza tipi tespiti sağlanmaktadır. Önerilen mimari, titreşim sinyallerini motor arıza teşhisinde hızlı ve güvenilir olarak kullanan etkin bir modeldir. Çalışma kapsamında farklı hız senaryoları kullanılarak eğitim ve test aşamalarının detaylı performans değerlendirmeleri sağlanmıştır. Genelleme kabiliyeti yüksek olan bu model ile, farklı senaryolarda yüksek doğruluk oranları ile arıza tespiti yapılmıştır.

Keywords

Derin öğrenme, konvolüsyonel sinir ağı, motor arıza tespiti

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