Gürültülü Sinyallerden Seyrek Ve Periyodik Arıza Verilerinin Çıkarılması

Öz

Bu çalışmada, rulmanlarda meydana gelen arızalara ait seyrek ve periyodik sinyal bileşenlerinin gürültülü gözlem verilerinden geri kazanımı incelenmektedir. Hedef sinyallerin darbeselliğini ve seyrekliğini artırmak amacıyla, yüksek mertebeden istatistiklerin (çarpıklık ve basıklık) optimizasyonu ile L_1-normu minimizasyonu yöntemleri karşılaştırılmıştır. İlgilenilen sinyal, harmonik olarak ilişkili sinüs dalgalarının toplamı şeklinde modellenmiş; bu optimizasyon fonksiyonlarının performansları, beyaz gürültü ve lineer bozulmanın bulunduğu ve bulunmadığı durumlarda değerlendirilmiştir. Elde edilen bulgular, gradyan tabanlı optimizasyonun istenmeyen bileşenleri bastırarak arıza kaynaklı periyodik darbeleri başarıyla geri kazandığını göstermektedir. Ayrıca sayısal deneyler, L_1-normu minimizasyonunun, çarpıklık ve basıklık maksimizasyonuna kıyasla, seyrekliği artırmada daha üstün bir performans ve daha hızlı yakınsama sergilediğini doğrulamaktadır.

Anahtar Kelimeler

• Seyrek sinyal geri kazanımı
• Gradyan arama optimizasyonu
• Arıza teşhisi
• Öznitelik çıkarımı

Extracting Sparse Periodic Fault Signatures From Noisy Signals

Öz

This study investigates the recovery of sparse, periodic fault signatures from noisy observations in rolling-element bearings. We compare the optimization of higher-order statistics (skewness and kurtosis) with L_1-norm minimization to enhance the impulsiveness and sparseness of the target signals. By modeling the signal of interest as a sum of harmonically related sinusoids, these objective functions are evaluated in the presence and absence of additive noise and linear distortion. The results demonstrate that gradient-based optimization successfully recovers defect-related periodic impulses by suppressing undesired components. Furthermore, numerical experiments confirm that L_1-norm minimization provides superior performance and faster convergence in promoting sparsity compared to skewness- and kurtosis-maximizing methods.

Anahtar Kelimeler

• Sparse signal recovery
• Gradient search optimization
• Fault diagnosis
• Feature extraction

Kaynakça

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