Progress in Detecting Transverse Matrix Cracking Using Modal Acoustic Emission

Gorman, Michael R.

doi:10.1007/978-1-4615-5339-7_72

Cited by 6 publications

(2 citation statements)

References 10 publications

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“…It requires broadband sensors with a nearly flat frequency response in the 50-3000 kHz frequency range. This AE technique is called Modal Acoustic Emission (MAE) [19,20] because it uses the plate wave theory as a theoretical background and analyses the waves according to their mechanical nature, namely extensional and flexural waves. The MAE technique allows a more convenient way to identify the damage mode by looking at the frequency content of the acoustic waves produced by the damage.…”

Section: Methodsmentioning

confidence: 99%

<title>Fiber optic sensor for continuous health monitoring in CFRP composite materials</title>

Rippert¹,

Papy²,

Wevers³

et al. 2002

Smart Structures and Materials 2002: Modeling, Signal Processing, and Control

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An intensity modulated sensor, based on the microbending concept, has been incorporated in laminates produced from a C/epoxy prepreg. Pencil lead break tests (Hsu-Neilsen sources) and tensile tests have been performed on this material. In this research study, fibre optic sensors will be proven to offer an alternative for the robust piezoelectric transducers used for Acoustic Emission (AE) monitoring. The main emphasis has been put on the use of advanced signal processing techniques based on time-frequency analysis. The signal Short Time Fourier Transform (STFT) has been computed and several robust noise reduction algorithms, such as Wiener adaptive filtering, improved spectral subtraction filtering, and Singular Value Decomposition (SVD) -based filtering, have been applied. An energy and frequency -based detection criterion is put forward to detect transient signals that can be correlated with Modal Acoustic Emission (MAE) results and thus damage in the composite material. There is a strong indication that time-frequency analysis and the Hankel Total Least Squares (HTLS) method can also be used for damage characterisation. This study shows that the signal from a quite simple microbend optical sensor contains information on the elastic energy released whenever damage is being introduced in the host material by mechanical loading. Robust algorithms can be used to retrieve and analyse this information.

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Section: Methodsmentioning

confidence: 99%

<title>Fiber optic sensor for continuous health monitoring in CFRP composite materials</title>

Rippert¹,

Papy²,

Wevers³

et al. 2002

Smart Structures and Materials 2002: Modeling, Signal Processing, and Control

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“…This provided a suitable means for recording transient AE signals with sampling rates in the megahertz range (now up to 40 MHz) and increasingly higher resolution of signal magnitude (from initially 12 bit up to 18 bit). High-resolution records of transient AE waves also rendered identification of the different wave modes in so-called "modal AE" feasible [8]. Recently, modal AE data were complemented by Finite Element Models (FEM) and Fast Fourier Transform (FFT) for mode identification [9].…”

Section: Introductionmentioning

confidence: 99%

Structural Health and Condition Monitoring with Acoustic Emission and Guided Ultrasonic Waves: What about Long-Term Durability of Sensors, Sensor Coupling and Measurement Chain?

Brunner

2021

Applied Sciences

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Acoustic Emission (AE) and Guided Ultrasonic Waves (GUWs) are non-destructive testing (NDT) methods in several industrial sectors for, e.g., proof testing and periodic inspection of pressure vessels, storage tanks, pipes or pipelines and leak or corrosion detection. In materials research, AE and GUW are useful for characterizing damage accumulation and microscopic damage mechanisms. AE and GUW also show potential for long-term Structural Health and Condition Monitoring (SHM and CM). With increasing computational power, even online monitoring of industrial manufacturing processes has become feasible. Combined with Artificial Intelligence (AI) for analysis this may soon allow for efficient, automated online process control. AI also plays a role in predictive maintenance and cost optimization. Long-term SHM, CM and process control require sensor integration together with data acquisition equipment and possibly data analysis. This raises the question of the long-term durability of all components of the measurement system. So far, only scant quantitative data are available. This paper presents and discusses selected aspects of the long-term durability of sensor behavior, sensor coupling and measurement hardware and software. The aim is to identify research and development needs for reliable, cost-effective, long-term SHM and CM with AE and GUW under combined mechanical and environmental service loads.

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