Stability of guided wave signals from bonded piezoelectric sensors

Vehorn, Keith A.; DeSimio, Martin P.; Olson, Steven E.; Brown, Kevin; Leonard, Matthew S

doi:10.1117/12.2009733

Cited by 2 publications

(3 citation statements)

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“…Reducing the raw sensor data to a damage index is necessary to establish a relationship with crack length. One commonly used damage index uses Pearson's correlation coefficient to relate the sensed signal from a damaged state to a baseline sensed signal (Vehorn, 2013).…”

Section: Structural Health Monitoring Systemmentioning

confidence: 99%

Machine Learning Based Prognostics of Fatigue Crack Growth in Notch Pre-cracked Aluminum 7075-T6 Rivet Hole

2018

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Constant stress amplitude fatigue tests were conducted on the notch pre-cracked Aluminum 7075-T6 rivet hole dog-bone coupons. Monitoring of visible surface crack length by special surface engraving using digital microscope images and by ultrasonic sensors signals was carried out to yield fatigue crack length measurements in relation to number of fatigue cycles applied. The experimental results provide ultrasonic sensor validation for fatigue crack length measurements. Fracto-graphic examination of failed fatigue surfaces has provided further confirmation of notch pre-crack length, crack initiation process, and crack growth marker bands. These experimental inputs were used in NASGRO and AFGROW software fatigue crack growth simulations. The simulation results did not match the crack initiation fatigue life measured by experiments. However, there was good agreement with crack growth simulations of larger cracks. Hence, we plan to develop a machine learning application that will learn the fatigue crack initiation and crack growth processes from data obtained from our own experiments and other fatigue data available from AFGROW databases. Nonlinear AutoRegressive models with eXogenous input (NARX) artificial neural network were used to predict crack growth longer than 5.0-mm. Particle filtering modeling with Bayesian updating was applied to these experimental data for prognostics of fatigue crack growth. A concept design and preliminary implementation results will be presented.

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Section: Structural Health Monitoring Systemmentioning

confidence: 99%

Machine Learning Based Prognostics of Fatigue Crack Growth in Notch Pre-cracked Aluminum 7075-T6 Rivet Hole

2018

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“…5 Since SHM Patch outputs data based on each actuator-sensor path (shown in Fig. 4), it is worth investigating which path is more sensitive to actual damage.…”

Section: Damage Index Algorithmsmentioning

confidence: 99%

“…Vehorn uses Pearson's correlation coefficient, 5 widely used in the field of statistics, to calculate a DI. The formulation of this index is…”

Section: Damage Index Algorithmsmentioning

confidence: 99%

Inspection Correlation Study of Ultrasonic-Based In Situ Structural Health Monitoring Monthly Report for December 2014-January 2015

Iglesias¹,

Haynes²,

Shiao³

2015

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Stability of guided wave signals from bonded piezoelectric sensors

Cited by 2 publications

References 0 publications

Machine Learning Based Prognostics of Fatigue Crack Growth in Notch Pre-cracked Aluminum 7075-T6 Rivet Hole

Machine Learning Based Prognostics of Fatigue Crack Growth in Notch Pre-cracked Aluminum 7075-T6 Rivet Hole

Inspection Correlation Study of Ultrasonic-Based In Situ Structural Health Monitoring Monthly Report for December 2014-January 2015

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