Dalgacık Dönüşümü ve Özbağlanım Model Parametreleri Öznitelikleri ile Otomobil Motor Seslerinden Arıza Tespiti

Year 2020, Volume: 3 Issue: 2, 48 - 54, 31.12.2020

Göktuğ Yılmaz Necip Fazıl Mete Umugabekazi Umusalama Önder Aydemir

Abstract

Keywords

Motor arızası

Supporting Institution

TÜBİTAK

Project Number

1139B411901576

Wavelet Transform and Autoregressive Model Parameter Features based Engine Fault Diagnosis System

Abstract

Örüntü tanıma ve makine öğrenmesi başarılı sonuçlar sağlamasından dolayı popülerliğini giderek arttırmakta ve birçok alanda kullanılmaktadır. Bu çalışmada marka ve model farkı gözetmeksizin zamanlama kayışı ve vuruntu arızası ile normal çalışma durumlarına ait otomobil motor sesleri sınıflandırılmıştır. Önerilen yöntem iki saniyelik motor seslerinden sürekli dalgacık dönüşümü ve özbağlanım parametresi özniteliklerin k-en yakın komşuluk algoritması ile sınıflandırarak %91.8 oranında sınıflandırma doğruluğu elde etmiştir. Elde edilen sonuçlar önerilen yöntemin otomobil motor sesleri kullanılarak araçlarda meydana gelebilecek arızaları büyük oranda tespit edilebileceğini göstermiştir. Böylece, otomobil motorlarındaki arızanın erken tespiti mümkün olmakta, bu da olası kazaların ve büyük arızaların ortaya çıkmasının önüne geçmektedir. Ayrıca, önerilen yöntemin araç yetkili servislerine ve motor ustalarına rehberlik etmek ve zaman kazandırmak amaçlı da kullanılabileceği düşünülmektedir.

Keywords

Motor arızası, Ses tanıma, Öznitelik Çıkarma

Project Number

1139B411901576

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