2005
DOI: 10.1109/tns.2004.843121
|View full text |Cite
|
Sign up to set email alerts
|

Structural integrity monitoring of steam generator tubing using transient acoustic signal analysis

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
4
1

Citation Types

0
6
0

Year Published

2008
2008
2024
2024

Publication Types

Select...
9

Relationship

0
9

Authors

Journals

citations
Cited by 15 publications
(6 citation statements)
references
References 15 publications
0
6
0
Order By: Relevance
“…Poorly suited for separating signals when their frequencies are too close. [4,10,11,35] Table 1. Cont.…”
Section: Signal Processing Techniquesmentioning
confidence: 99%
See 1 more Smart Citation
“…Poorly suited for separating signals when their frequencies are too close. [4,10,11,35] Table 1. Cont.…”
Section: Signal Processing Techniquesmentioning
confidence: 99%
“…Some of the issues that stem from this involve the elimination of dispersive modes, mode separation and defect identification, as well as their classification. To enhance and improve the detection and classification of defects, several techniques have been employed to process the input signal such as time-frequency representation, including the reassigned spectrogram and the Winger-Ville distribution, wavelet analysis, Hilbert-Huang transforms and cross-correlation techniques [7][8][9][10][11].…”
Section: Introductionmentioning
confidence: 99%
“…Structural health monitoring (SHM) has proven to be a valuable system for evaluating the abnormal conditions of structures such as nuclear power plants [1,2] and steam generators [3]. It primarily focuses on implementing damage identification strategies and is considered a promising system for assessing structural damage in real time [4,5].…”
Section: Introductionmentioning
confidence: 99%
“…To provide the passive control of thermo-acoustic instability, the slope confinement method is utilized [22]. In addition, the acoustic emission method, which, in recent years, needs less data [23], is used for failure detection in the tubes of steam generators, boilers and heat exchangers [24]. Moreover, bidirectional long short-term memory recurrent neural networks [25] and the deep learning flexible boundary regression method can be used with acoustic emission signals to enhance leak detection in boiler tubes [26].…”
Section: Introductionmentioning
confidence: 99%