scholarly journals A Modified Empirical Wavelet Transform for Acoustic Emission Signal Decomposition in Structural Health Monitoring

Sensors ◽  
2018 ◽  
Vol 18 (5) ◽  
pp. 1645 ◽  
Author(s):  
Shaopeng Dong ◽  
Mei Yuan ◽  
Qiusheng Wang ◽  
Zhiling Liang
Keyword(s):  
Acoustic Emission ◽  
Wavelet Transform ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Acoustic Emission Signal ◽  
Signal Decomposition ◽  
Structural Health ◽  
Emission Signal ◽  
Empirical Wavelet Transform

scholarly journals Fibre Bragg Grating Based Acoustic Emission Measurement System for Structural Health Monitoring Applications

Materials ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 897
Author(s):  
Sagar Jinachandran ◽  
Ginu Rajan
Keyword(s):  
Acoustic Emission ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Market Potential ◽  
Bragg Grating ◽  
Fibre Bragg Grating ◽  
Emission Measurement ◽  
Structural Health ◽  
Sensing Systems ◽  
Recent Trends

Fiber Bragg grating (FBG)-based acoustic emission (AE) detection and monitoring is considered as a potential and emerging technology for structural health monitoring (SHM) applications. In this paper, an overview of the FBG-based AE monitoring system is presented, and various technologies and methods used for FBG AE interrogation systems are reviewed and discussed. Various commercial FBG AE sensing systems, SHM applications of FBG AE monitoring, and market potential and recent trends are also discussed.

Assessment of characteristics of acoustic emission parameters for valve damage detection under varying compressor speeds

2020 ◽  
Vol 234 (17) ◽  
pp. 3521-3540
Author(s):  
Hoi Yin Sim ◽  
Rahizar Ramli ◽  
Ahmad Saifizul
Keyword(s):  
Acoustic Emission ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Acoustic Emission Signal ◽  
Discrete Wavelet ◽  
Mean Square ◽  
Crest Factor ◽  
Emission Signal ◽  
Best Parameter ◽  
All Speeds

Acoustic emission technique is often employed to detect valve abnormalities. With the development of technology, machine learning-based fault diagnosis methods are prevalent in the nondestructive testing industry as they can automatically detect valve problems without any human intervention. Nevertheless, feeding in all possible input parameters into the learning algorithm without any prior assessment may result in high computational cost and time, while adding to the risk of having false alarms. This study intended to obtain characteristics of acoustic emission signal for various valve conditions and compressor speeds by examining the four most commonly used parameters, namely the acoustic emission root mean square, acoustic emission crest factor, acoustic emission variance, and acoustic emission kurtosis. The study begins with time–frequency analysis of one revolution acoustic emission signal acquired from a faulty suction valve through discrete wavelet transform to obtain the signal characteristics of valve events. To associate signals with valve movements, the reconstructed discrete wavelet transform signals are further segregated into six time segments, and the four acoustic emission parameters are computed from each of the time segments. These parameters are analyzed through statistical analysis namely the two-way analysis of variance, followed by the Tukey test to obtain the best parameter which can differentiate each valve condition clearly at all speeds. The results revealed that acoustic emission root mean square is the best parameter especially in identification of heavy grease valve condition during suction valve opening event while acoustic emission crest factor is capable to detect leaky valve during the suction valve closing event at all speeds. It is believed that effective valve diagnosis strategy can be delivered by referring to the features of parameters and the characteristic valve event timing corresponding to each valve condition and speed.

scholarly journals Optimisation of acoustic emission wavestreaming for structural health monitoring

2020 ◽  
Vol 19 (6) ◽  
pp. 2007-2022
Author(s):  
John P McCrory ◽  
Matthew R Pearson ◽  
Rhys Pullin ◽  
Karen M Holford
Keyword(s):  
Acoustic Emission ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Peak Frequency ◽  
Image Correlation ◽  
The Novel ◽  
Structural Health ◽  
Growth Activity ◽  
Materials Used ◽  
Novel Concept

Structural health monitoring has gained wide appeal for applications with high inspection costs, such as aircraft and wind turbines. As the structures and materials used in these industries evolve, so too must the technologies used to monitor them. Acoustic emission is a passive method of detecting damage which lends itself well to structural health monitoring. One form of acoustic emission monitoring, known as wavestreaming, involves intermittently recording data for set periods of time and using the sequential recordings to detect changes in the state of the structure. However, at present, there is no standard method for selecting appropriate wavestream recording parameters, such as their length or their interval of collection. This article investigates a method of optimising acoustic emission wavestreaming for structural health monitoring purposes by introducing the novel concept of adjoining consecutive discrete acoustic emission hit signals to create synthetic wavestreams. To this end, a pre-notched 492 mm × 67.5 mm × 20 mm, 300M grade steel cantilever specimen was subject to cyclic loading and both acoustic emission hit data and conventional wavestreams were collected as a crack grew in the notched region; crack growth activity was also monitored using digital image correlation for comparison. To demonstrate the proposed optimisation process, four sets of synthetic wavestreams were created from the hit data, 0.25, 0.5, 1.0 and 1.5 s in length, and compared with the 1.5-s-long conventional wavestreams. The activity of the peak frequency and frequency centroid bands of interest within the conventional and synthetic wavestreams were examined to determine whether or not cracking activity could be inferred through them. Across comparisons of all data, it was found that the 0.5-s-long synthetic wavestreams contained enough information to identify the same trends as the conventional wavestreams for this application; thus, the use of synthetic wavestreams as a tool for selecting an appropriate wavestream recording length was demonstrated.

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