Geri Dön

Blind adaptive extraction of impulsive signatures from sound and vibration signals

Başlık çevirisi mevcut değil.

PDF İndir

  1. Tez No: 508369
  2. Yazar: AZİZ KUBİLAY OVACIKLI
  3. Danışmanlar: Prof. JOHAN E. CARLSON, Dr. PATRIK PAAJARVI
  4. Tez Türü: Doktora
  5. Konular: Elektrik ve Elektronik Mühendisliği, Electrical and Electronics Engineering
  6. Anahtar Kelimeler: Belirtilmemiş.
  7. Yıl: 2017
  8. Dil: İngilizce
  9. Üniversite: Luleå University of Technology
  10. Enstitü: Yurtdışı Enstitü
  11. Ana Bilim Dalı: Belirtilmemiş.
  12. Bilim Dalı: Belirtilmemiş.
  13. Sayfa Sayısı: 180

Özet

Özet yok.

Özet (Çeviri)

The two main questions in science,“why”and“how,”are answered here in the context of statistical signal processing applied to vibration analysis and ultrasonic testing for fault detection and characterization in critical materials such as rolling bearings and thin layered media. Both types of materials are of interest in industrial processes. In such processes, it is important to ensure the best operating conditions for rolling bearings and good product quality for thin layered materials. The methods defended in this thesis are designed for the retrieval of impulsive signals, which represent either faults or responses to excitation, arising from such equipment and materials. Because the measurements collected via sensors usually consist of signals masked by unknown systems and noise, retrieving the information-rich portion of such a signal is often challenging. By exploiting the statistical characteristics of these signals due to their natural structure, a linear system is designed to recover the signals of interest in different scenarios. The primary approach is to suppress undesired components while enhancing impulsive events through iterative adaptation of a filter. Signal recovery is accomplished by optimizing an objective (skewness) that quantify the presumed characteristics, raising the questions of the objective surface topology and the probability of ill convergence. To address these questions, mathematical proofs, experimental evidence and comprehensive discussions are presented in the presented contributions, each aiming to answer a specific question. The purpose of the theoretical study is to fill a gap in signal processing research by providing analytical and numerical results, especially on skewness surface characteristics, for a signal model (periodic impulses) built based on harmonically related sinusoids. With an understanding of the inner workings and the sufficient conditions, the same approach is applied to different classes of signals encountered during ultrasonic testing, such as aperiodic finite energy signals (material impulse response) and very-short-duration impulses acting as excitations. A similar approach aimed at enhancing another attribute, sparseness, is investigated through numerical experiments on ultrasonic echoes as a case study. In summary, an objective quantifying a certain characteristic is optimized to recover the signal portions that carry valuable information buried in noisy vibrations and ultrasonic measurements. Considering that research is deemed successful if it creates more questions than it answers and allows ideas to flourish, thereby creating scientific value, the presented work aims to achieve this in the context of statistical signal processing. Analytical derivations supplemented by experiments form the basis for observations, discussions and further questions to be studied with regard to similar phenomena arising from different sources in nature.

Benzer Tezler

  1. Tez No

    684391

    Derin öğrenme ile modülasyon sınıflandırması

    Modulation classification with deep learning

    SELÇUK BALSÜZEN

    Yüksek Lisans

    Türkçe

    Türkçe

    2021

    Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrolİstanbul Teknik Üniversitesi

    Elektronik ve Haberleşme Mühendisliği Ana Bilim Dalı

    PROF. DR. MESUT KARTAL

  2. Tez No

    892819

    Öz bilgi destekli derin öğrenme yaklaşımları ile hsg gürültü giderme

    Self-ınformation empowered deep learning approaches for hsı denoising

    ORHAN TORUN

    Doktora

    Türkçe

    Türkçe

    2024

    Elektrik ve Elektronik MühendisliğiHacettepe Üniversitesi

    Elektrik-Elektronik Mühendisliği Ana Bilim Dalı

    DOÇ. DR. SENİHA ESEN YÜKSEL ERDEM

    PROF. DR. MEHMET ERKUT ERDEM

  3. Tez No

    585613

    Face recognition by using feature extraction structures

    Özellık çıkarımı kullanarak yüz tanıma potansiyeli

    AUMED MUHSIN ABBAS

    Yüksek Lisans

    İngilizce

    İngilizce

    2019

    Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve KontrolBahçeşehir Üniversitesi

    Bilgisayar Mühendisliği Ana Bilim Dalı

    DR. ÖĞR. ÜYESİ Tarkan AYDIN

  4. Tez No

    711812

    ارائه یک روش واترمارکینگ کور براساس کوانتیزه کردندرخت ویولت

    A blind watermarking based on quantization ofwavelet trees

    MESUT MELEK

    Yüksek Lisans

    Farsça

    Farsça

    2010

    Elektrik ve Elektronik MühendisliğiIslamic Azad University

    Elektrik ve Elektronik Mühendisliği Ana Bilim Dalı

    YRD. DOÇ. DR. SİAMAK HAGHİPOUR

  5. Tez No

    119056

    Blind adaptive decision feedback equalization

    Gözü kapalı uyarlanır karar geri beslemeli denkleştirme

    ESRA KURBAN

    Yüksek Lisans

    İngilizce

    İngilizce

    2002

    Elektrik ve Elektronik MühendisliğiOrta Doğu Teknik Üniversitesi

    Elektrik ve Elektronik Mühendisliği Ana Bilim Dalı

    DOÇ. DR. BUYURMAN BAYKAL

Geri Dön