scholarly journals Using Acoustic Emission Measurements for Ice-Melting Detection

2019 ◽  
Vol 9 (24) ◽  
pp. 5387
Author(s):  
Michael Stamm ◽  
Helge Pfeiffer ◽  
Johan Reynaert ◽  
Martine Wevers
Keyword(s):  
Acoustic Emission ◽  
New Technologies ◽  
Daily Basis ◽  
Detection Systems ◽  
Non Invasive ◽  
Fuel Tanks ◽  
Water Accumulation ◽  
Ice Detection ◽  
The Time Domain ◽  
Ae Signals

Aircraft operators being faced with water accumulation in fuel tanks on a daily basis and are looking for reliable detection systems to determine the remaining amount of accumulated ice during maintenance after flight. Using such a technology, an increase in the safety and efficiency of the aircraft operation would be possible in this highly competitive market. This article presents the use of the Acoustic Emission Technique (AE) for the reliable and non-invasive monitoring of the melting of ice in fuel tanks. This technology is in principle based on the fact that a phase transition comes frequently along with stress relaxation that can be used for monitoring the process. Therefore, the melting of water can, in essence, be monitored with AE without accessing the ice directly. The analysis of the AE signals has been carried out in the time domain since it was the melting of ice needed to be monitored as a function of temperature rise time. The insights presented in this paper can possibly lead to new technologies for ice detection, especially in remote areas that are not easily accessible with other techniques.

scholarly journals Undervoltage Identification in Three Phase Induction Motor Using Low-Cost Piezoelectric Sensors and STFT Technique

Proceedings ◽  
2019 ◽  
Vol 42 (1) ◽  
pp. 72
Author(s):  
Leonardo Carvalho ◽  
Guilherme Lucas ◽  
Marco Rocha ◽  
Claudio Fraga ◽  
Andre Andreoli
Keyword(s):  
Acoustic Emission ◽  
Induction Motor ◽  
Large Scale ◽  
New Technologies ◽  
Low Cost ◽  
Industrial Applications ◽  
Piezoelectric Sensors ◽  
Time Frequency ◽  
Three Phase ◽  
Ae Signals

Three-phase induction motors (IMs) are electrical machines used on a large scale in industrial applications because they are versatile, robust and low maintenance devices. However, IMs are significantly affected when fed by unbalanced voltages. Prolonged operation under voltage unbalance (VU) conditions degrades performance and shortens machine life by producing imbalances in stator currents that abnormally raise winding temperature. With the development of new technologies and research on non-destructive techniques (NDT) for fault diagnoses in IMs, it is relevant to obtain economically accessible, efficient and reliable sensors capable of acquiring signals that allow the identification of this type of failure. The objective of this study is to evaluate the application of low-cost piezoelectric sensors in the acquisition of acoustic emission (AE) signals and the identification of VU through the analysis of short-term Fourier transform (STFT) spectrograms. The piezoelectric sensor makes NDT feasible, as it is an affordable and inexpensive component. In addition, STFT allows time-frequency analyses of acoustic emission signals. In this NDT, two sensors were coupled on both sides of an induction motor frame. The AE signals obtained during the IM operation were processed and the resulting spectrograms were analyzed to identify the different VU levels. After comparing the AE signals for faulty conditions with the signals for the IM operating at balanced voltages, it was possible to obtain a desired identification that confirmed the successful application of low-cost piezoelectric sensors for VU condition detection in three-phase induction machines.

Manufacturing and Analysis of 3-D Woven Sandwich Composite Under Compressive Loading With Acoustic Emission Monitoring

2002 ◽  
Author(s):  
M. Islam ◽  
H. Mahfuz ◽  
M. Motuku ◽  
M. Saha ◽  
E. Ayorinde ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Polyurethane Foam ◽  
Compressive Loading ◽  
Piezoelectric Sensor ◽  
Woven Fabric ◽  
Sandwich Composite ◽  
Displacement Curve ◽  
Compression Tests ◽  
The Time Domain ◽  
Ae Signals

Composite sandwich panel with 3-d woven fabric preform were manufactured through a vacuum assisted resin transfer molding (VARTM) process. The 3-d sandwich fabric preform known as Parabeam, consist of two bi-directional woven E-glass fabric surfaces, which are inter connected mechanically with several vertical woven piles. Parabeam provides a new type of sandwich composite that possess several multifunctional benefits in addition to light weight, bending stiffness and core skin debonding resistance advantages. After consolidation of the panels, polyurethane foam was injected into the core. Conventional sandwich panels were also fabricated with precast polyurethane foam and E-glass face sheet for comparison with the Parabeam sandwich. Edge wise compression tests were conducted and the corresponding acoustic emission (AE) responses were monitored by an advance AE system. Piezoelectric sensor was used to detect the AE signals. On the basis AE signals, the damage source of the specimen were identified, and classified. Furthermore, cracking size were estimated and used to determine the delamination events among the AE signals. Consequently, the AE signals were correlated to the load-displacement curve in the time domain in order to elucidate the types of damage in the specimen. Details of the manufacturing process, the compression tests, and the analysis of the AE signals are presented in this paper.

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

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.

scholarly journals Acoustic Emission Testing and Ib-Value Analysis of Ultraviolet Light-Irradiated Fiber Composites

2021 ◽  
Vol 11 (14) ◽  
pp. 6550
Author(s):  
Doyun Jung ◽  
Wonjin Na
Keyword(s):  
Acoustic Emission ◽  
Irradiation Time ◽  
Tensile Load ◽  
Failure Behavior ◽  
Value Analysis ◽  
Emission Testing ◽  
Final Failure ◽  
Acoustic Emission Testing ◽  
Woven Glass Fiber ◽  
Ae Signals

The failure behavior of composites under ultraviolet (UV) irradiation was investigated by acoustic emission (AE) testing and Ib-value analysis. AE signals were acquired from woven glass fiber/epoxy specimens tested under tensile load. Cracks initiated earlier in UV-irradiated specimens, with a higher crack growth rate in comparison to the pristine specimen. In the UV-degraded specimen, a serrated fracture surface appeared due to surface hardening and damaged interfaces. All specimens displayed a linearly decreasing trend in Ib-values with an increasing irradiation time, reaching the same value at final failure even when the starting values were different.

Acoustic Emission Monitoring of Leaks in Pipes for Transport of Liquid and Gaseous Media: A Model Experiment

2006 ◽  
Vol 13-14 ◽  
pp. 351-356 ◽  
Author(s):  
Andreas J. Brunner ◽  
Michel Barbezat
Keyword(s):  
Acoustic Emission ◽  
Model Experiment ◽  
Structural Integrity ◽  
Fiber Composite ◽  
Leak Testing ◽  
Pipe Segment ◽  
Potential Applications ◽  
Gaseous Media ◽  
The Media ◽  
Ae Signals

In order to explore potential applications for Active Fiber Composite (AFC) elements made from piezoelectric fibers for structural integrity monitoring, a model experiment for leak testing on pipe segments has been designed. A pipe segment made of aluminum with a diameter of 60 mm has been operated with gaseous (compressed air) and liquid media (water) for a range of operating pressures (between about 5 and 8 bar). Artificial leaks of various sizes (diameter) have been introduced. In the preliminary experiments presented here, commercial Acoustic Emission (AE) sensors have been used instead of the AFC elements. AE sensors mounted on waveguides in three different locations have monitored the flow of the media with and without leaks. AE signals and AE waveforms have been recorded and analysed for media flow with pressures ranging from about 5 to about 8 bar. The experiments to date show distinct differences in the FFT spectra depending on whether a leak is present or not.

scholarly journals A Principal Component Analysis of Acoustic Emission Signals from a Landing Gear Component

2008 ◽  
Vol 13-14 ◽  
pp. 41-47 ◽  
Author(s):  
Rhys Pullin ◽  
Mark J. Eaton ◽  
James J. Hensman ◽  
Karen M. Holford ◽  
Keith Worden ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Acoustic Emission ◽  
Background Noise ◽  
Principal Component ◽  
Component Analysis ◽  
Landing Gear ◽  
Fracture Detection ◽  
Artificial Fracture ◽  
High Level ◽  
Ae Signals

This work forms part of a larger investigation into fracture detection using acoustic emission (AE) during landing gear airworthiness testing. It focuses on the use of principal component analysis (PCA) to differentiate between fracture signals and high levels of background noise. An artificial acoustic emission (AE) fracture source was developed and additionally five sources were used to generate differing AE signals. Signals were recorded from all six artificial sources in a real landing gear component subject to no load. Further to this, artificial fracture signals were recorded in the same component under airworthiness test load conditions. Principal component analysis (PCA) was used to automatically differentiate between AE signals from different source types. Furthermore, successful separation of artificial fracture signals from a very high level of background noise was achieved. The presence of a load was observed to affect the ultrasonic propagation of AE signals.

Grinding Acoustic Emission Classification in Terms of Mechanical Behaviours

2007 ◽  
Vol 329 ◽  
pp. 15-20 ◽  
Author(s):  
Xun Chen ◽  
James Griffin
Keyword(s):  
Neural Network ◽  
Acoustic Emission ◽  
Material Removal ◽  
Grinding Process ◽  
Short Time Fourier Transform ◽  
Fundamental Investigation ◽  
Mechanical Behaviours ◽  
Wavelets Transform ◽  
Short Time ◽  
Ae Signals

The material removal in grinding involves rubbing, ploughing and cutting. For grinding process monitoring, it is important to identify the effects of these different phenomena experienced during grinding. A fundamental investigation has been made with single grit cutting tests. Acoustic Emission (AE) signals would give the information relating to the groove profile in terms of material removal and deformation. A combination of filters, Short-Time Fourier Transform (STFT), Wavelets Transform (WT), statistical windowing of the WT with the kurtosis, variance, skew, mean and time constant measurements provided the principle components for classifying the different grinding phenomena. Identification of different grinding phenomena was achieved from the principle components being trained and tested against a Neural Network (NN) representation.

Virus induced hepatocellular carcinoma (HCC) and protein biomarkers

2021 ◽  
Author(s):  
Hamza Abbas Jaffari ◽  
Sumaira Mazhar
Keyword(s):  
Hepatocellular Carcinoma ◽  
Early Stage ◽  
Imaging Techniques ◽  
Disease Diagnosis ◽  
Protein Biomarkers ◽  
Early Disease ◽  
Detection Systems ◽  
Non Invasive ◽  
Viral Hepatitis B ◽  
Current Article

Hepatocellular carcinoma (HCC) is a standout amongst the most widely recognized cancers around the world, and just as the alcoholic liver disease it is also progressed by extreme viral hepatitis B or C. At the early stage of the disease, numerous patients are asymptomatic consequently late diagnosis of HCC occurs resulting in expensive surgical resection or transplantation. On the basis of the alpha fetoprotein (AFP) estimation, combined with the ultrasound and other sensitive imaging techniques used, the non-invasive detection systems are available. For early disease diagnosis and its use in the effective treatment of HCC patients, the identification of HCC biomarkers has provided a breakthrough utilizing the molecular genetics and proteomics. In the current article, most recent reports on the protein biomarkers of HBV or HCV-related HCC and their co-evolutionary association with liver cancer are reviewed.

Experiment on Surface Roughness and Acoustic Emission Signal in Infeed Period of Cylindrical Plunge Grinding

2013 ◽  
Vol 690-693 ◽  
pp. 2442-2445 ◽  
Author(s):  
Hao Lin Li ◽  
Hao Yang Cao ◽  
Chen Jiang
Keyword(s):  
Surface Roughness ◽  
Acoustic Emission ◽  
Acoustic Emission Signal ◽  
Feed Rate ◽  
Grinding Process ◽  
Experiment Research ◽  
Plunge Grinding ◽  
Emission Signal ◽  
Ae Signal ◽  
Ae Signals

This work presents an experiment research on Acoustic emission (AE) signal and the surface roughness of cylindrical plunge grinding with the different infeed time. The changed infeed time of grinding process is researched as an important parameter to compare AE signals and surface roughnesses with the different infeed time in the grinding process. The experiment results show the AE signal is increased by the increased feed rate. In the infeed period of the grinding process, the surface roughness is increased at first, and then is decreased.

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