scholarly journals Thermographic Inspection of Internal Defects in Steel Structures: Analysis of Signal Processing Techniques in Pulsed Thermography

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6015
Author(s):  
Yoonjae Chung ◽  
Ranjit Shrestha ◽  
Seungju Lee ◽  
Wontae Kim
Keyword(s):  
Signal Processing ◽  
Power Plants ◽  
Principal Component ◽  
Steel Structures ◽  
High Energy ◽  
Internal Defects ◽  
Pulsed Thermography ◽  
Processing Techniques ◽  
Signal Processing Techniques ◽  
Defect Detectability

This study performed an experimental investigation on pulsed thermography to detect internal defects, the major degradation phenomena in several structures of the secondary systems in nuclear power plants as well as industrial pipelines. The material losses due to wall thinning were simulated by drilling flat-bottomed holes (FBH) on the steel plate. FBH of different sizes in varying depths were considered to evaluate the detection capability of the proposed technique. A short and high energy light pulse was deposited on a sample surface, and an infrared camera was used to analyze the effect of the applied heat flux. The three most established signal processing techniques of thermography, namely thermal signal reconstruction (TSR), pulsed phase thermography (PPT), and principal component thermography (PCT), have been applied to raw thermal images. Then, the performance of each technique was evaluated concerning enhanced defect detectability and signal to noise ratio (SNR). The results revealed that TSR enhanced the defect detectability, detecting the maximum number of defects, PPT provided the highest SNR, especially for the deeper defects, and PCT provided the highest SNR for the shallower defects.

scholarly journals Latest Advances in Common Signal Processing of Pulsed Thermography for Enhanced Detectability: A Review

2021 ◽  
Vol 11 (24) ◽  
pp. 12168
Author(s):  
Yoonjae Chung ◽  
Seungju Lee ◽  
Wontae Kim
Keyword(s):  
Signal Processing ◽  
Infrared Thermography ◽  
Heat Diffusion ◽  
Thermal Excitation ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Pulsed Thermography ◽  
Processing Techniques ◽  
Non Destructive ◽  
Signal Processing Techniques

Non-destructive testing (NDT) is a broad group of testing and analysis techniques used in science and industry to evaluate the properties of a material, structure, or system for characteristic defects and discontinuities without causing damage. Recently, infrared thermography is one of the most promising technologies as it can inspect a large area quickly using a non-contact and non-destructive method. Moreover, thermography testing has proved to be a valuable approach for non-destructive testing and evaluation of structural stability of materials. Pulsed thermography is one of the active thermography technologies that utilizes external energy heating. However, due to the non-uniform heating, lateral heat diffusion, environmental noise, and limited parameters of the thermal imaging system, there are some difficulties in detecting and characterizing defects. In order to improve this limitation, various signal processing techniques have been developed through many previous studies. This review presents the latest advances and exhaustive summary of representative signal processing techniques used in pulsed thermography according to physical principles and thermal excitation sources. First, the basic concept of infrared thermography non-destructive testing is introduced. Next, the principle of conventional pulsed thermography and signal processing technologies for non-destructive testing are reviewed. Then, we review advances and recent advances in each signal processing. Finally, the latest research trends are reviewed.

Improving the Robustness of Odor Sensing Systems by Multivariate Signal Processing

2012 ◽  
pp. 296-316 ◽  
Author(s):  
Marta Padilla ◽  
Jordi Fonollosa ◽  
Santiago Marco
Keyword(s):  
Principal Component Analysis ◽  
Signal Processing ◽  
New Technologies ◽  
Principal Component ◽  
Statistical Signal Processing ◽  
Electronic Noses ◽  
Sensing Systems ◽  
Data Arrangement ◽  
Processing Techniques ◽  
Signal Processing Techniques

Electronic noses or artificial olfaction systems based on chemical gas sensors present lack of robustness, a problem that is mainly technological and requires more research to improve fabrication processes and develop new technologies. However, statistical signal processing can help to mathematically reduce those unwanted effects on the sensors responses before the prediction step. In this chapter, the authors explore the concept of robustness in electronic nose instruments and the use of several multivariate signal processing techniques to deal with two specific problems related to such lack of robustness: time instability (drift) and the detection of a possible faulty sensor in the array. In particular, three different techniques that deal with drift problems are reviewed. These techniques address drift by correction of unwanted variance, by taking advantage of the characteristics of a three-way data arrangement, or by using a blind strategy to extract information with chemical meaning. Finally, a method based on principal component analysis is presented for fault detection, faulty sensor identification, and correction of a fault in a sensor array.

Analysis of signal processing techniques in pulsed thermography

2013 ◽  
Author(s):  
Fernando Lopez ◽  
Clemente Ibarra-Castanedo ◽  
Xavier Maldague ◽  
Vicente de Paulo Nicolau
Keyword(s):  
Signal Processing ◽  
Pulsed Thermography ◽  
Processing Techniques ◽  
Signal Processing Techniques

Taking Advantage of Signal Processing Techniques for the Life Management of NPP Components

2006 ◽  
Author(s):  
L. Chatellier ◽  
S. Dubost ◽  
F. Peisey ◽  
B. Richard ◽  
L. Fournier ◽  
...  
Keyword(s):  
Signal Processing ◽  
Eddy Current ◽  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Industry ◽  
Destructive Testing ◽  
Small Surface ◽  
Plant Operator ◽  
Processing Techniques ◽  
Signal Processing Techniques

The long term management of nuclear power plants raises several major issues among which the aging management of key components ranks high, from both technical and economic points of view. In order to detect and characterize potential defects on cast components, a program of in-service inspections is carried out by non-destructive testing (NDT) methods. In general, defect detection is the first step of an inspection procedure. Should a defect be detected, the plant operator must evaluate whether the component should be replaced or repaired (now or later) and will be required to prove that the component still meets regulatory requirements. That is why the characterization of the defect in terms of locating and sizing is essential, especially when the proof relies on mechanical calculations. In this paper we provide an overview of advanced signal processing techniques based on regularization of inverse problems. Those techniques have a strong potential for improving defect positioning and sizing. This has already been demonstrated in several R&D studies in the field of radiography and ultrasonics, leading in some cases to expertise-oriented applications. After a presentation of the general principles, we detail how regularization can be applied to process eddy current probe signals and provide good estimates of the depth of small surface breaking defects. Encouraging laboratory results have been obtained so far, which may lead to re-consider the scope of the eddy current technique as presently used in the nuclear industry. For example, its eligibility as an alternative NDE method could be explored in cases dealing with this kind of defect, if ultrasonics failed to meet the required characterization performance.

scholarly journals Performance of signal processing techniques for anomaly detection using a temperature-based measurement interpretation approach

2020 ◽  
Author(s):  
Rolands Kromanis ◽  
Prakash Kripakaran
Keyword(s):  
Signal Processing ◽  
Anomaly Detection ◽  
Thermal Response ◽  
Principal Component ◽  
Response Prediction ◽  
National Physical Laboratory ◽  
Physical Laboratory ◽  
Time Histories ◽  
Processing Techniques ◽  
Signal Processing Techniques

Abstract This study investigates the effectiveness of four signal processing techniques in supporting a data-driven strategy for anomaly detection that relies on correlations between measurements of bridge response and temperature distributions. The strategy builds upon the regression-based thermal response prediction methodology which was developed by the authors to accurately predict thermal response from distributed temperature measurements. The four techniques that are investigated as part of the strategy are moving fast Fourier transform, moving principal component analysis, signal subtraction method and cointegration method. The techniques are compared on measurement time histories from a laboratory structure and a footbridge at the National Physical Laboratory. Results demonstrate that anomaly events can be detected successfully depending on the magnitude and duration of the event and the choice of an appropriate anomaly detection technique.

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