Parameters Based AE Analysis

Aggelis, Dimitrios G.; Shiotani, Tomoki

doi:10.1007/978-3-030-67936-1_4

Cited by 6 publications

(6 citation statements)

References 83 publications

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“…The burst signal onset is defined as the instant at which the absolute voltage exceeds the predetermined threshold level. It is considered that the burst signal has ended when a determined time, called the Hit Lockout Time, has passed without any further signal crossing the threshold [30]. The burst signal from its onset to its end is called a hit.…”

Section: Parameter-based Analysismentioning

confidence: 99%

“…Parameter analysis is based in the extraction of descriptors that contain most of the waveform information, which are the following ones (Figure 4) [30]: Approaches for analysing AE signals can be divided in two groups: parameter-based analysis and signal-based analysis. In the first approach, as the signal is acquired, the characteristic parameters that describe the waveforms of the different parts of the signal (hits) are computed, and even the whole hit can be recorded; thus, parameter-based analysis can be used with burst signals only.…”

Section: Parameter-based Analysismentioning

confidence: 99%

“…As different wavelet functions result in different output results, the Empirical Mode Decomposition (EMD) method was introduced in 1998 [34] to reduce the impact of a pre-defined wavelet function. EMD decomposes any complex dataset into a finite Parameter analysis is based in the extraction of descriptors that contain most of the waveform information, which are the following ones (Figure 4) [30]:…”

Section: Time-frequency Analysismentioning

confidence: 99%

“…Neural networks (NNs) consist of an input and output layers that are connected through a number of hidden layers, all mathematically related via non-linear relationships that must be estimated (learning) [30]. Convolutional neural networks (CNNs) are defined as NNs that use the convolutional operation, which is defined as the dot product between a grid-structured set of numbers (weights) and similar grid-structured inputs drawn from different spatial locations in the input [42].…”

Section: Machine Learning Approachesmentioning

confidence: 99%

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Review of the Uses of Acoustic Emissions in Monitoring Cavitation Erosion and Crack Propagation

Fernández-Osete,

Bermejo,

Ayneto-Gubert

et al. 2024

Foundations

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Nowadays, hydropower plants are being used to compensate for the variable power produced by the new fluctuating renewable energy sources, such as wind and solar power, and to stabilise the grid. Consequently, hydraulic turbines are forced to work more often in off-design conditions, far from their best efficiency point. This new operation strategy increases the probability of erosive cavitation and of hydraulic instabilities and pressure fluctuations that increase the risk of fatigue damage and reduce the life expectancy of the units. To monitor erosive cavitation and fatigue damage, acoustic emissions induced by very-high-frequency elastic waves within the solid have been traditionally used. Therefore, acoustic emissions are becoming an important tool for hydraulic turbine failure detection and troubleshooting. In particular, artificial intelligence is a promising signal analysis research hotspot, and it has a great potential in the condition monitoring of hydraulic turbines using acoustic emissions as a key factor in the digitalisation process. In this paper, a brief introduction of acoustic emissions and a description of their main applications are presented. Then, the research works carried out for cavitation and fracture detection using acoustic emissions are summarised, and the different levels of development are compared and discussed. Finally, the role of artificial intelligence is reviewed, and expected directions for future works are suggested.

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Section: Parameter-based Analysismentioning

confidence: 99%

Section: Parameter-based Analysismentioning

confidence: 99%

Section: Time-frequency Analysismentioning

confidence: 99%

Section: Machine Learning Approachesmentioning

confidence: 99%

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Review of the Uses of Acoustic Emissions in Monitoring Cavitation Erosion and Crack Propagation

Fernández-Osete,

Bermejo,

Ayneto-Gubert

et al. 2024

Foundations

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show abstract

“…The reason for this is that UWS monitoring offers higher control and repeatability of the measurements. [20] Amplitude (A), Duration (D), Energy (E), Rise time (R), Average frequency (AF), Peak frequency (PF), Time-of-arrival (TOA)…”

Section: Introductionmentioning

confidence: 99%

Combining Active and Passive Stress Wave Monitoring Techniques: Opportunities for Condition Assessment of Concrete Structures

Schumacher¹,

Niederleithinger²

2022

eJNDT

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Condition monitoring of concrete structures has experienced increasing interest over the last decades, especially for bridges. In this presentation, a combined approach based on active and passive stress wave monitoring techniques is proposed and discussed. While from a wave propagation mechanics point of view, acoustic emission (AE) and ultrasonic monitoring are the same, they differ fundamentally in what the cause of the stress wave is. For the former, external stimuli such as overloading or temperature cause the spontaneous release of fracture energy in unknown locations. As a result, AE events cannot be repeated. For the latter, a known source at a known location is employed at a specified time. In contrast, this approach is highly controlled and repeatable. The presenter argues that a combination of these two techniques has the potential to provide a much more comprehensive picture of ongoing fracture processes as well as slowly occurring changes in the structure. An overview and comparison of the techniques is provided, along with observations and results from laboratory experiments and field tests that the presenter has been involved in over the last 15 years.

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Acoustic Emission Characteristics of Coal Failure Under Triaxial Loading and Unloading Disturbance

et al. 2022

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Parameters Based AE Analysis

Cited by 6 publications

References 83 publications

Review of the Uses of Acoustic Emissions in Monitoring Cavitation Erosion and Crack Propagation

Review of the Uses of Acoustic Emissions in Monitoring Cavitation Erosion and Crack Propagation

Combining Active and Passive Stress Wave Monitoring Techniques: Opportunities for Condition Assessment of Concrete Structures

Acoustic Emission Characteristics of Coal Failure Under Triaxial Loading and Unloading Disturbance

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