CHF 135.00

Rail Crack Monitoring Using Acoustic Emission Technique

Inglese · Copertina rigida

Spedizione di solito entro 6 a 7 settimane

Descrizione

Ulteriori informazioni

This thesis provides an innovative strategy for rail crack monitoring using the acoustic emission (AE) technique. The field study presented is a significant improvement on laboratory studies in the literature in terms of complex rail profile and crack conditions as well as high operational noise. AE waves induced by crack propagation, crack closure, wheel-rail impact and operational noise were obtained through a series of laboratory and field tests, and analyzed by wavelet transform (WT) and synchrosqueezed wavelet transform (SWT). A wavelet power-based index and the enhanced SWT scalogram were sequentially proposed to classify AE waves induced by different mechanisms according to their energy distributions in the time-frequency domain. A novel crack sizing method taking advantage of crack closure-induced AE waves was developed based on fatigue tests in the laboratory. The propagation characteristics of AE waves in the rail were investigated, and Tsallis synchrosqueezed wavelet entropy(TSWE) with time was finally brought forward to detect and locate rail cracks in the field. The proposed strategy for detection, location and sizing of rail cracks helps to ensure the safe and smooth operation of the railway system. This thesis is of interest to graduate students, researchers and practitioners in the area of structural health monitoring.

Riassunto

This thesis provides an innovative strategy for rail crack monitoring using the acoustic emission (AE) technique. The field study presented is a significant improvement on laboratory studies in the literature in terms of complex rail profile and crack conditions as well as high operational noise. AE waves induced by crack propagation, crack closure, wheel-rail impact and operational noise were obtained through a series of laboratory and field tests, and analyzed by wavelet transform (WT) and synchrosqueezed wavelet transform (SWT). A wavelet power-based index and the enhanced SWT scalogram were sequentially proposed to classify AE waves induced by different mechanisms according to their energy distributions in the time–frequency domain. A novel crack sizing method taking advantage of crack closure-induced AE waves was developed based on fatigue tests in the laboratory. The propagation characteristics of AE waves in the rail were investigated, and Tsallis synchrosqueezed wavelet entropy(TSWE) with time was finally brought forward to detect and locate rail cracks in the field. The proposed strategy for detection, location and sizing of rail cracks helps to ensure the safe and smooth operation of the railway system. This thesis is of interest to graduate students, researchers and practitioners in the area of structural health monitoring.

Dettagli sul prodotto

Autori Dan Li
Editore Springer, Berlin
 
Contenuto Libro
Forma del prodotto Copertina rigida
Data pubblicazione 30.04.2018
Categoria Scienze naturali, medicina, informatica, tecnica > Tecnica > Meccanica, tecnica di produzione
 
EAN 9789811083471
ISBN 978-981-10-8347-1
Numero di pagine 136
Illustrazioni XXVIII, 136 p. 99 illus., 94 illus. in color.
Dimensioni (della confezione) 15.7 x 24.2 x 1.5 cm
Peso (della confezione) 400 g
 
Serie Springer Theses
Springer Theses
Categorie Werkstoffprüfung, B, Acoustics, Wellenmechanik (Vibration und Akustik), engineering, Testing of materials, Characterization and Analytical Technique, Materials science, Characterization and Evaluation of Materials, Wave mechanics (vibration & acoustics), Engineering Acoustics, Acoustical engineering, Rail Defects
 

Recensioni dei clienti

Per questo articolo non c'è ancora nessuna recensione. Scrivi la prima recensione e aiuta gli altri utenti a scegliere.

Scrivi una recensione

Top o flop? Scrivi la tua recensione.

Per i messaggi a CeDe.ch si prega di utilizzare il modulo di contatto.

I campi contrassegnati da * sono obbligatori.

Inviando questo modulo si accetta la nostra dichiarazione protezione dati.