Ortak uzamsal örüntü özniteliklerine dayalı yeni bir rulman arızası teşhisi yaklaşımı

Year 2023, Volume: 12 Issue: 4, 1545 - 1557, 15.10.2023

Nurhan Gürsel Özmen , Yunus Emre Karabacak

https://doi.org/10.28948/ngumuh.1330864

Abstract

Makinelerde durum izleme, erken arıza tespiti, bakım süreçlerinin optimize edilmesi ve iş sürekliliğinin sağlanması açısından büyük öneme sahiptir. Bu çalışmada, rulmanlar için yeni bir akıllı tespit yaklaşımı sunulmuş, rulman arızalarıyla ilgili ayırt edici öznitelikleri çıkarmak için Ortak Uzamsal Örüntü (OUÖ) yöntemi kullanılmıştır. Farklı kaynaklardan gelen sinyal matrislerinin varyans oranını maksimize eden OUÖ, geleneksel frekans temelli özniteliklerden ayrılır. Bu teknik, her ölçümde benzersiz titreşim desenlerini yakalayarak arızalı ve sağlam rulmanlar arasındaki farkı belirlemeyi sağlar. Önerilen yöntemin etkinliği Yapay Sinir Ağı (YSA), Destek Vektör Makinesi (DVM) ve K-En Yakın Komşu (k-YK) algoritmaları kullanılarak iki farklı veri kümesinde değerlendirildi. Sonuçlar, YSA kullanıldığında iki sınıflı arıza tespitinde %88.5 doğruluk ve arıza sınıflandırmasında %93.5 doğruluk elde edilebileceğini gösterdi. Geleneksel zaman alanı öznitelikleri ile yapılan karşılaştırma, OUÖ özniteliklerinin üstün performansını ortaya koydu. OUÖ iki sınıflı ve çoklu sınıflı senaryolarda yüksek doğruluk oranları sergiledi. Böylece, OUÖ öznitelikleri titreşim verileri aracılığıyla rulman arızalarının etkili bir şekilde tespit edilmesi için umut verici bir yol olarak ortaya çıkmaktadır.

Keywords

Ortak Uzamsal Örüntü, Rulmanlar, Durum İzleme, Titreşim Sinyalleri

A new bearing fault diagnosis approach based on common spatial pattern features

Abstract

Condition monitoring in machines holds significant importance for early fault detection, optimizing maintenance processes, and ensuring operational continuity. In this study, a novel intelligent detection approach for rolling bearings is introduced, utilizing the Common Spatial Pattern (CSP) method to extract distinctive features related to bearing faults. By maximizing the variance ratio of signal matrices from distinct sources, CSP sets itself apart from conventional frequency-based features. This technique captures characteristic vibration patterns unique to each measurement, enabling differentiation between faulty and healthy bearings. The effectiveness of the proposed method was assessed using Artificial Neural Network (ANN), Support Vector Machine (SVM), and K-Nearest Neighbour (k-NN) algorithms across two diverse datasets. The results indicated an 88.5% accuracy in two-class fault detection and 93.5% in fault classification when employing ANN. Comparison with traditional time domain feature sets highlighted the superior performance of CSP features, exhibiting elevated accuracy rates in both two-class and multiclass scenarios. Thus, CSP features emerge as a promising avenue for effectively monitoring bearing conditions through vibration data.

Keywords

Common Spatial Pattern, Bearings, Condition Monitoring, Vibration Signals

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