On the crack tip propagation monitoring within wood material: Cluster analysis of acoustic emission data compared with numerical modelling

Diakhaté, Malick; Angellier, Nicolas; Pitti, Rostand Moutou; Dubois, Frédéric

doi:10.1016/j.conbuildmat.2017.09.065

Cited by 27 publications

(16 citation statements)

References 16 publications

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“…Nowadays, AE 136 becomes a very accurate and efficient method for fault, leaks and fatigue detection and monitoring techniques in materials 137,138 and structural analysis. 139 That include, concrete, 140,141 plastics, 142–144 polymers, 145–147 ceramics, 148–150 pipelines, 151–153 pressure vessels, 154,155 storage tanks, 156–159 bridges, 160–162 aircraft, 163,164 bucket trucks, 165 wood, 166–168 fiberglass, 169–172 composites, 173–175 welding, 176–178 , tubes, 179 aerospace structures 180–182 and finally for military applications. 183–185 Figure 2 illustrates AE instrumentation chain.…”

Section: General Background Of Ae Analysismentioning

confidence: 99%

Sounds and acoustic emission-based early fault diagnosis of induction motor: A review study

AlShorman

Alkahatni

Masadeh

et al. 2021

Advances in Mechanical Engineering

109

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Nowadays, condition-based maintenance (CBM) and fault diagnosis (FD) of rotating machinery (RM) has a vital role in the modern industrial world. However, the remaining useful life (RUL) of machinery is crucial for continuous monitoring and timely maintenance. Moreover, reduced maintenance costs, enhanced safety, efficiency, reliability, and availability are the main important industrial issues to maintain valuable and high-cost machinery. Undoubtedly, induction motor (IM) is considered to be a pivotal component in industrial machines. Recently, acoustic emission (AE) becomes a very accurate and efficient method for fault, leaks and fatigue detection and monitoring techniques. Moreover, CM and FD based on the AE of IM have been growing over recent years. The proposed research study aims to review condition monitoring (CM) and fault diagnosis (FD) studies based on sound and AE for four types of faults: bearings, rotor, stator, and compound. The study also points out the advantages and limitations of using sound and AE analysis in CM and FD. Existing public datasets for AE based analysis for CM and FD of IM are also mentioned. Finally, challenges facing AE based CM and FD for RM, especially for IM, and possible future works are addressed in this study.

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Section: General Background Of Ae Analysismentioning

confidence: 99%

Sounds and acoustic emission-based early fault diagnosis of induction motor: A review study

AlShorman

Alkahatni

Masadeh

et al. 2021

Advances in Mechanical Engineering

109

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“…Roberts and Talebzadeh [13] found that the energy of the signal is less affected by the threshold setting and coupling conditions and the change in the signal energy value can reflect the phase changes inside the material. With the development of acoustic emission technology, many researchers have used acoustic emission technology to study the fatigue process of different materials, such as stainless steel [14], aluminum alloy [15], titanium alloy [16], self-compacting concrete [17], wood material [18], and SIC/SIC composites [19]. In recent years, many researchers have conducted acoustic emission studies on the tensile behavior of various materials and achieved certain results.…”

Section: Introductionmentioning

confidence: 99%

Study of the Tensile Damage of 321 Stainless Steel for Solar Thermal Power Generation by Acoustic Emission

Liao

Tan

2020

International Journal of Photoenergy

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In this paper, the acoustic emission technique was used to study the signal during the tensile damage of 321 stainless steel for solar thermal power generation. It was found that the peak frequency can effectively distinguish different types of signals during the tensile test. The interference signals generated during the tensile test are effectively resolved by combining the amplitude-peak frequency distribution map and the energy-peak frequency distribution map. The amplitude-time map of the acoustic emission signal is successfully divided into three stages by using peak frequency parameter.

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“…The main difference between AE sensing and other non-destructive methods is the capability to inspect in real time, since continuous monitoring in situ can be carried out, and the onset and progression of damage to a structure can be assessed, even when this is not possible by means of visual inspection. Some applications of the AE method to the study of wood behavior can be found in [ 21 , 22 , 23 , 24 , 25 , 26 ], dealing with detection of failure in adhesive joints and evaluation of fractures and its propagation in wood.…”

Section: Introductionmentioning

confidence: 99%

Monitoring of Carbon Fiber-Reinforced Old Timber Beams via Strain and Multiresonant Acoustic Emission Sensors

Rescalvo

Valverde-Palacios

Suárez

et al. 2018

Sensors

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This paper proposes the monitoring of old timber beams with natural defects (knots, grain deviations, fissures and wanes), reinforced using carbon composite materials (CFRP). Reinforcement consisted of the combination of a CFRP laminate strip and a carbon fabric discontinuously wrapping the timber element. Monitoring considered the use and comparison of two types of sensors: strain gauges and multi-resonant acoustic emission (AE) sensors. Results demonstrate that: (1) the mechanical behavior of the beams can be considerably improved by means of the use of CFRP (160% in bending load capacity and 90% in stiffness); (2) Acoustic emission sensors provide comparable information to strain gauges. This fact points to the great potential of AE techniques for in-service damage assessment in real wood structures.

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On the crack tip propagation monitoring within wood material: Cluster analysis of acoustic emission data compared with numerical modelling

Cited by 27 publications

References 16 publications

Sounds and acoustic emission-based early fault diagnosis of induction motor: A review study

Sounds and acoustic emission-based early fault diagnosis of induction motor: A review study

Study of the Tensile Damage of 321 Stainless Steel for Solar Thermal Power Generation by Acoustic Emission

Monitoring of Carbon Fiber-Reinforced Old Timber Beams via Strain and Multiresonant Acoustic Emission Sensors

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