TİTREŞİM SİNYALLERİNDEN RULMAN ARIZALARIN TESPİT EDİLMESİ

Year 2025, Volume: 13 Issue: 3, 295 - 306

Eyyüp Akcan

https://doi.org/10.17694/bajece.1757057

Abstract

Döner makinelerdeki rulmanlar, sistem güvenliği ve operasyonel süreklilik açısından kritik öneme sahip mekanik bileşenlerdir. Bu çalışmada, Case Western Reserve University (CWRU) rulman veri seti kullanılarak dört farklı makine öğrenmesi algoritması olan Random Forest, XGBoost, Support Vector Machine (SVM) ve Naive Bayes modelleri ile rulman arıza sınıflandırması gerçekleştirilmiştir. Zaman alanında çıkarılan istatistiksel öznitelikler temelinde, her modelin doğruluk (accuracy), kesinlik (precision), duyarlılık (recall) ve F1-skoru metrikleri ile performansları değerlendirilmiş ve karşılaştırılmıştır. Bulgular, özellikle Random Forest ve XGBoost algoritmalarının %95.73 doğruluk ve %96 precision, recall ve F1-score ile üstün performans sergilediğini ortaya koymuştur. SVM modeli %93.73 doğrulukla güvenilir bir alternatif olarak değerlendirilirken, Naive Bayes algoritması %92.40 doğrulukla nispeten daha düşük performans göstermiştir. Ayrıca, istatistiksel özniteliklerin tekil sınıflandırma başarımı incelenmiş ve özellikle standart sapma (sd) ve RMS gibi özniteliklerin yüksek katkı sunduğu belirlenmiştir. Bu çalışma, geleneksel makine öğrenmesi algoritmalarının farklı öznitelik yapılarına göre performanslarını detaylı biçimde analiz ederek, rulman arızalarının erken ve doğru tespiti için karar vericilere yol gösterici bir referans sunmaktadır.

Keywords

Rulman arıza teşhisi , Makine öğrenmesi , Random Forest , XGBoost , Destek Vektör Makineleri , Naive Bayes , CWRU veri seti.

Detection of Bearing Faults from Vibration Signals

Abstract

Bearings are critical mechanical components in rotating machinery, playing a vital role in system safety and operational continuity. In this study, the Case Western Reserve University (CWRU) bearing dataset is used to perform fault classification using four machine learning algorithms: Random Forest, XGBoost, Support Vector Machine (SVM), and Naive Bayes. Based on statistical features extracted in the time domain, the performance of each model is evaluated using accuracy, precision, recall, and F1-score metrics. The results reveal that Random Forest and XGBoost algorithms achieved superior performance with 95.73% accuracy and 96% in precision, recall, and F1-score. The SVM model, with 93.73% accuracy, stands out as a robust alternative, while the Naive Bayes algorithm shows relatively lower performance with 92.40% accuracy. Additionally, an individual feature-based classification analysis indicates that standard deviation (sd) and root mean square (RMS) features contribute most significantly to model performance. This study provides a comprehensive performance analysis of traditional machine learning algorithms, offering a valuable reference for early and accurate detection of bearing faults.

Keywords

Bearing fault diagnosis , Machine learning , Random Forest , XGBoost , Support Vector Machine , Naive Bayes , CWRU dataset.

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