3D boiler tube leak detection technique using acoustic emission signals for power plant structure health monitoring

2011 ◽  
Author(s):  
Dong-Hyun Kim ◽  
Bo-Suk Yang ◽  
Sang-Bum Lee
Keyword(s):  
Acoustic Emission ◽  
Power Plant ◽  
Health Monitoring ◽  
Leak Detection ◽  
Detection Technique ◽  
Boiler Tube ◽  
Plant Structure ◽  
Structure Health Monitoring

Fiber-optic intrinsic distributed acoustic emission sensor for large structure health monitoring

2009 ◽  
Vol 34 (12) ◽  
pp. 1858 ◽  
Author(s):  
Sheng Liang ◽  
Chunxi Zhang ◽  
Wentai Lin ◽  
Lijing Li ◽  
Chen Li ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Health Monitoring ◽  
Fiber Optic ◽  
Large Structure ◽  
Structure Health Monitoring ◽  
Acoustic Emission Sensor

scholarly journals A Novel Pipeline Leak Detection Technique Based on Acoustic Emission Features and Two-Sample Kolmogorov–Smirnov Test

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8247
Author(s):  
Akhand Rai ◽  
Zahoor Ahmad ◽  
Md Junayed Hasan ◽  
Jong-Myon Kim
Keyword(s):  
Acoustic Emission ◽  
Leak Detection ◽  
Acoustic Emission Event ◽  
Mean Value ◽  
Detection Technique ◽  
Continuous Type ◽  
Kolmogorov Smirnov ◽  
Mean Variance ◽  
Ae Signals ◽  
Ks Test

Pipeline leakage remains a challenge in various industries. Acoustic emission (AE) technology has recently shown great potential for leak diagnosis. Many AE features, such as root mean square (RMS), peak value, standard deviation, mean value, and entropy, have been suggested to detect leaks. However, background noise in AE signals makes these features ineffective. The present paper proposes a pipeline leak detection technique based on acoustic emission event (AEE) features and a Kolmogorov–Smirnov (KS) test. The AEE features, namely, peak amplitude, energy, rise-time, decay time, and counts, are inherent properties of AE signals and therefore more suitable for recognizing leak attributes. Surprisingly, the AEE features have received negligible attention. According to the proposed technique, the AEE features are first extracted from the AE signals. For this purpose, a sliding window was used with an adaptive threshold so that the properties of both burst- and continuous-type emissions can be retained. The AEE features form distribution that change its shape when the pipeline condition changes from normal to leakage. The AEE feature distributions for leak and healthy conditions were discriminated using the two-sample KS test, and a pipeline leak indicator (PLI) was obtained. The experimental results demonstrate that the developed PLI accurately distinguishes the leak and no-leak conditions without any prior leak information and it performs better than the traditional features such as mean, variance, RMS, and kurtosis.

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