Determining the feasibility of identifying creep rupture of stainless steel cladding tubes on-line using acoustic emission technique

Purpose – The purpose of this paper is to determine the feasibility of identifying the creep rupture of reactor cladding tubes using acoustic emission technique (AET). Design/methodology/approach – The creep rupture tests were carried out by pressuring stainless steel capsules upto 6 MPa at room temperature and then heating continuously in a furnace upto rupture. The acoustic emission (AE) signals generated during the creep rupture tests were recorded using a 150 kHz resonant sensor and analysed using AE Win software. Findings – When rupture occurs in the pressurized capsule tube representing the cladding tube, AE sensor attached to a waveguide captures the mechanical disturbance from the capsule and these data can be advantageously used to identify the creep rupture event of the cladding tube. Practical implications – The creep rupture data of fuel clad tube is very important in design and for smooth operation of nuclear reactors without fuel pin failure in reactors. Originality/value – AE is an advanced non-destructive evaluation technique. This technique has been successfully applied for on-line monitoring of creep rupture of the reactor cladding tube which otherwise could be detected by thermocouple readings only.

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Detection of corrosion on 304 stainless steel by acoustic emission measurement

Anti-Corrosion Methods and Materials ◽

10.1108/00035591011040083 ◽

2010 ◽

Vol 57 (3) ◽

pp. 126-132 ◽

Cited By ~ 16

Author(s):

Gang Du ◽

Weikui Wang ◽

Shizhe Song ◽

Shijiu Jin

Keyword(s):

Stainless Steel ◽

Acoustic Emission ◽

Constant Load ◽

Corrosion Process ◽

304 Stainless Steel ◽

Emission Measurement ◽

Nacl Solution ◽

Content Type ◽

Ae Signal ◽

Ae Signals

PurposeThe purpose of this paper is to report an investigation of the acoustic emission (AE) characteristics of the corrosion process of 304 stainless steel in acidic NaCl solution.Design/methodology/approachThe corrosion behavior of a specimen with constant load in acidic NaCl solution was studied, and the AE signal characteristics of the corrosion process were analyzed. Stress corrosion cracking of the specimen was detected using the AE and electrochemical noise (EN) techniques, and the acquired data were compared.FindingsThe results indicated that AE technology is very sensitive to the AE signals generated by 304 nitrogen controlled stainless steel in acidic NaCl solution. The characteristics of AE signals at different stages of the corrosion process are significantly different. Additionally, the AE test result is confirmed by the EN test results.Originality/valueThe characteristics of AE signals at different stages of the corrosion process are gained for the first time, which is an important guide by which to distinguishing different stages of corrosion.

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Analysis of Acoustic Emission (AE) Signals for Quality Monitoring of Laser Lap Microwelding

Applied Sciences ◽

10.3390/app11157045 ◽

2021 ◽

Vol 11 (15) ◽

pp. 7045

Author(s):

Ming-Chyuan Lu ◽

Shean-Juinn Chiou ◽

Bo-Si Kuo ◽

Ming-Zong Chen

Keyword(s):

Stainless Steel ◽

Acoustic Emission ◽

Frequency Domain ◽

Monitoring System ◽

Welding Quality ◽

Steel Sheets ◽

Signal Features ◽

Ae Signal ◽

Laser Microwelding ◽

Ae Signals

In this study, the correlation between welding quality and features of acoustic emission (AE) signals collected during laser microwelding of stainless-steel sheets was analyzed. The performance of selected AE features for detecting low joint bonding strength was tested using a developed monitoring system. To obtain the AE signal for analysis and develop the monitoring system, lap welding experiments were conducted on a laser microwelding platform with an attached AE sensor. A gap between the two layers of stainless-steel sheets was simulated using clamp force, a pressing bar, and a thin piece of paper. After the collection of raw signals from the AE sensor, the correlations of welding quality with the time and frequency domain features of the AE signals were analyzed by segmenting the signals into ten 1 ms intervals. After selection of appropriate AE signal features based on a scatter index, a hidden Markov model (HMM) classifier was employed to evaluate the performance of the selected features. Three AE signal features, namely the root mean square (RMS) of the AE signal, gradient of the first 1 ms of AE signals, and 300 kHz frequency feature, were closely related to the quality variation caused by the gap between the two layers of stainless-steel sheets. Classification accuracy of 100% was obtained using the HMM classifier with the gradient of the signal from the first 1 ms interval and with the combination of the 300 kHz frequency domain signal and the RMS of the signal from the first 1 ms interval.

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Monitoring of 13% Cr martensitic stainless steel corrosion in chloride solution in presence of thiosulphate by acoustic emission technique

Corrosion Science ◽

10.1016/j.corsci.2016.05.010 ◽

2016 ◽

Vol 111 ◽

pp. 151-161 ◽

Cited By ~ 21

Author(s):

Luigi Calabrese ◽

Massimiliano Galeano ◽

Edoardo Proverbio ◽

Domenico Di Pietro ◽

Filippo Cappuccini ◽

...

Keyword(s):

Stainless Steel ◽

Acoustic Emission ◽

Chloride Solution ◽

Martensitic Stainless Steel ◽

Steel Corrosion ◽

Acoustic Emission Technique

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Reciprocating compressor valve damage estimation under varying speeds through the acoustic emission technique

International Journal of Structural Integrity ◽

10.1108/ijsi-03-2019-0024 ◽

2019 ◽

Vol 10 (5) ◽

pp. 621-633

Author(s):

Hoi-Yin Sim ◽

Rahizar Ramli ◽

Ahmad Saifizul

Keyword(s):

Acoustic Emission ◽

Regression Analysis ◽

Flow Rate ◽

Reciprocating Compressor ◽

Damage Estimation ◽

Content Type ◽

Air Compressor ◽

Ae Signal ◽

Valve Opening ◽

Ae Signals

Purpose The purpose of this paper is to examine the effect of reciprocating compressor speeds and valve conditions on the roor-mean-square (RMS) value of burst acoustic emission (AE) signals associated with the physical motion of valves. The study attempts to explore the potential of AE signal in the estimation of valve damage under varying compressor speeds. Design/methodology/approach This study involves the acquisition of AE signal, valve flow rate, pressure and temperature at the suction valve of an air compressor with speed varrying from 450 to 800 rpm. The AE signals correspond to one compressor cycle obtained from two simulated valve damage conditions, namely, the single leak and double leak conditions are compared to those of the normal valve plate. To examine the effects of valve conditions and speeds on AE RMS values, two-way analysis of variance (ANOVA) is conducted. Finally, regression analysis is performed to investigate the relationship of AE RMS with the speed and valve flow rate for different valve conditions. Findings The results showed that AE RMS values computed from suction valve opening (SVO), suction valve closing (SVC) and discharge valve opening (DVO) events are significantly affected by both valve conditions and speeds. The AE RMS value computed from SVO event showed high linear correlation with speed compared to SVC and DVO events for all valve damage conditions. As this study is conducted at a compressor running at freeload, increasing speed of compressor also results in the increment of flow rate. Thus, the valve flow rate can also be empirically derived from the AE RMS value through the regression method, enabling a better estimation of valve damages. Research limitations/implications The experimental test rig of this study is confined to a small pressure ratio range of 1.38–2.03 (free-loading condition). Besides, the air compressor is assumed to be operated at a constant speed. Originality/value This study employed the statistical methods namely the ANOVA and regression analysis for valve damage estimation at varying compressor speeds. It can enable a plant personnel to make a better prediction on the loss of compressor efficiency and help them to justify the time for valve replacement in future.

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Research on Acoustic Emission Signals Characteristics of Pitting Corrosion on 304 Stainless Steel

Volume 1B: Codes and Standards ◽

10.1115/pvp2013-97302 ◽

2013 ◽

Author(s):

Zhongzheng Zhang ◽

Hua Liang ◽

Cheng Ye ◽

Wensheng Cai ◽

Jun Jiang ◽

...

Keyword(s):

Stainless Steel ◽

Acoustic Emission ◽

Pitting Corrosion ◽

High Frequency ◽

Low Frequency ◽

Ferric Chloride Solution ◽

304 Stainless Steel ◽

Flexural Wave ◽

Higher Temperature ◽

Ae Signals

In order to study acoustic emission (AE) signals waveform characteristics of pitting corrosion on 304 stainless steel under higher temperature than lower one, Pitting corrosion process on 304 stainless steel in 6% ferric chloride solution at 70°C was monitored by AE technology. Wavelet transform and mode acoustic emission technology were combined to deal with recorded AE signals, and micromorphologic observation was performed for further verification. The results showed that signal waveform was mainly composed of low-frequency (<100KHz) flexural wave with larger amplitude & energy and high-frequency (>100KHz) expansion wave with lesser amplitude & energy. The research results have some certain significance for AE monitoring of pitting corrosion on 304 stainless steel.

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Identification of corrosion mechanisms on 13% Cr martensitic stainless steel in thiosulphate containing chloride solution by acoustic emission technique

International Journal of Microstructure and Materials Properties ◽

10.1504/ijmmp.2018.097781 ◽

2018 ◽

Vol 13 (6) ◽

pp. 403

Author(s):

Luigi Calabrese ◽

Massimiliano Galeano ◽

Edoardo Proverbio

Keyword(s):

Stainless Steel ◽

Acoustic Emission ◽

Chloride Solution ◽

Martensitic Stainless Steel ◽

Acoustic Emission Technique ◽

Corrosion Mechanisms

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Fatigue behavior and damage analysis by the acoustic emission technique of cross-ply laminates under tensile loading

Multidiscipline Modeling in Materials and Structures ◽

10.1108/mmms-08-2016-0036 ◽

2017 ◽

Vol 13 (2) ◽

pp. 150-164

Author(s):

Imen Ben Ammar ◽

Abderrahim El Mahi ◽

Chafik Karra ◽

Rachid El Guerjouma ◽

Mohamed Haddar

Keyword(s):

Acoustic Emission ◽

Mechanical Behavior ◽

Fatigue Behavior ◽

Glass Fibers ◽

Tensile Tests ◽

Hybrid Fibers ◽

Content Type ◽

Laminated Materials ◽

Ae Signals ◽

Damage Investigation

Purpose The purpose of this paper is to study the mechanical behavior in fatigue tensile mode of different cross-ply laminates constituted of unidirectional carbon fibers, hybrid fibers and glass fibers in an epoxy matrix; and to identify and characterize the local damage in the laminated materials with the use of the acoustic emission (AE) technique. Design/methodology/approach The tests in the fatigue mode permitted the determination of the effect of the stacking sequences, thickness of 90° oriented layers and reinforcement types on the fatigue mechanical behavior of the laminated materials. The damage investigation in those materials is reached with the analysis of AE signals collected from fatigue tensile tests. Findings The results show the effects of reinforcement type, stacking sequences and thicknesses ratio of 90° and 0° layers on the mechanical behavior. A cluster analysis of AE data is achieved and the resulting clusters are correlated with the damage mechanism of specimens under loading tests. Originality/value The analysis of AE signals collected from tensile tests of the fatigue failure load allows the damage investigation in different types of cross-ply laminates which are differentiated by the reinforcement type, stacking sequences and thicknesses ratio of 90° and 0° layers.

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Classification of Corrosion Detected by Acoustic Emission

Nondestructive Evaluation ◽

10.1115/imece2003-41334 ◽

2003 ◽

Cited By ~ 3

Author(s):

N. Saenkhum ◽

A. Prateepasen ◽

P. Keawtrakulpong

Keyword(s):

Neural Network ◽

Stainless Steel ◽

Acoustic Emission ◽

Pitting Corrosion ◽

Classification Performance ◽

Feed Forward Neural Network ◽

Source Mechanisms ◽

Visual Characteristic ◽

Verification Techniques ◽

Ae Signals

This paper presents an Acoustic Emission (AE) to detect pitting corrosion in stainless steel. The AE signals were analyzed to reveal the correlation between AE parameters and severity levels of pitting corrosion in austenitic stainless steel 304 (SS304). In this work, the corrosion severity is graded roughly into five levels based on the depth of corrosion. Relationships between a number of time-domain AE parameters and the corrosion severity were first studied and key parameters identified. The corrosion severity was also categorized into three stages: initial, propagation and final stages based on the source mechanisms of the AE signals. We identified these stages from the frequency-domain characteristic of the AE signal and the visual characteristic of the corroded pits in each level of corrosion severity. A number of measures were employed to quantify such characteristics and the source mechanisms hypothesized. To demonstrate the usefulness of such parameters, a feed-forward neural network was used to classify the corrosion severity. Preprocessing and verification techniques were provided to facilitate and to maintain the generalization capability of the network. The classification performance is excellent and demonstrates that the AE technique and a neural network can be efficiently used to detect and monitor the occurrence of corrosion as well as to classify the corrosion severity.

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