scholarly journals Use of Acoustic Emission and Pattern Recognition for Crack Detection of a Large Carbide Anvil

Sensors ◽  
2018 ◽  
Vol 18 (2) ◽  
pp. 386 ◽  
Author(s):  
Bin Chen ◽  
Yanan Wang ◽  
Zhaoli Yan
Keyword(s):  
Pattern Recognition ◽  
Acoustic Emission ◽  
Crack Detection ◽  
Large Carbide

scholarly journals Crack detection in riveted lap joints using fiber laser acoustic emission sensors

2017 ◽  
Vol 25 (16) ◽  
pp. 19457 ◽  
Author(s):  
G. A. Cranch ◽  
L. Johnson ◽  
M. Algren ◽  
S. Heerschap ◽  
G. A. Miller ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Fiber Laser ◽  
Crack Detection ◽  
Lap Joints ◽  
Acoustic Emission Sensors ◽  
Riveted Lap Joints

Pattern recognition approach for acoustic emission burst detection in a gearbox under different operating conditions

2019 ◽  
Vol 25 (17) ◽  
pp. 2295-2304
Author(s):  
Félix Leaman ◽  
Cristián Molina Vicuña ◽  
Ralph Baltes ◽  
Elisabeth Clausen
Keyword(s):  
Pattern Recognition ◽  
Acoustic Emission ◽  
Condition Monitoring ◽  
Rotational Speed ◽  
Operating Conditions ◽  
Burst Detection ◽  
Threshold Method ◽  
Planetary Gearbox ◽  
Industrial Sectors ◽  
Artificial Neural Network Ann

Diverse machines in the mining, energy, and other industrial sectors are subject to variable operating conditions (OCs) such as rotational speed and load. Therefore, the condition monitoring techniques must be adapted to face this scenario. Within these techniques, the acoustic emission (AE) technology has been successfully used as a technique for condition monitoring of components such as gears and bearings. An AE analysis involves the detection of transients within the signals, which are called AE bursts. Traditional methods for AE burst detection are based on the definition of threshold values. When the machine under study works under variable rotational speed and load, threshold-based methods could produce inadequate results due to the influence of these OCs on the AE. This paper presents a novel burst detection method based on pattern recognition using an artificial neural network (ANN) for classification. The results of the method were compared to an adaptive threshold method. Experimental data were measured in a planetary gearbox test rig under different OCs. The results showed that both methods perform similarly when signals measured under constant OCs are considered. However, when signals are measured under different OCs, the ANN method performs better. Thus, the comparative analysis showed the good potential of the approach to improve an AE analysis of variable speed and/or load machines.

scholarly journals Recognition Algorithm of Acoustic Emission Signals Based on Conditional Random Field Model in Storage Tank Floor Inspection Using Inner Detector

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Yibo Li ◽  
Yuxiang Zhang ◽  
Huiyu Zhu ◽  
Rongxin Yan ◽  
Yuanyuan Liu ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Acoustic Emission ◽  
Random Field ◽  
Storage Tank ◽  
Field Model ◽  
Conditional Random Field ◽  
Recognition Algorithm ◽  
Support Vector ◽  
Random Field Model ◽  
Conditional Random Field Model

Acoustic emission (AE) technique is often used to detect inaccessible area of large storage tank floor with AE sensors placed outside the tank. For tanks with fixed roofs, the drop-back signals caused by condensation mix with corrosion signals from the tank floor and interfere with the online AE inspection. The drop-back signals are very difficult to filter out using conventional methods. To solve this problem, a novel AE inner detector, which works inside the storage tank, is adopted and a pattern recognition algorithm based on CRF (Conditional Random Field) model is presented. The algorithm is applied to differentiate the corrosion signals from interference signals, especially drop-back signals caused by condensation. Q235 steel corrosion signals and drop-signals were collected both in laboratory and in field site, and seven typical AE features based on hits and frequency are extracted and selected by mRMR (Minimum Redundancy Maximum Relevance) for pattern recognition. To validate the effectiveness of the proposed algorithm, the recognition result of CRF model was compared with BP (Back Propagation), SVM (Support Vector Machine), and HMM (Hidden Markov Model). The results show that training speed, accuracy, and ROC (Receiver Operating Characteristic) results of the CRF model outperform other methods.

scholarly journals Transverse Crack Detection in 3D Angle Interlock Glass Fibre Composites Using Acoustic Emission

Materials ◽  
2016 ◽  
Vol 9 (8) ◽  
pp. 699 ◽  
Author(s):  
Matthieu Gresil ◽  
Mohamed Saleh ◽  
Constantinos Soutis
Keyword(s):  
Acoustic Emission ◽  
Glass Fibre ◽  
Crack Detection ◽  
Transverse Crack ◽  
Fibre Composites
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