Structural Health Monitoring and Impact Detection Using Neural Networks for Damage Characterization

Ross, R.

doi:10.2514/6.2006-2117

Cited by 6 publications

(10 citation statements)

References 8 publications

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“…The two different type of piezoelectric sensors used are shown in Figure 2 and their signal output amplitude are measured by direct connection to a digital oscilloscope; the comparison of the amplitudes is reported in Figure 3 for different impact energy from 0 to 45 mJ. In Figure 3 we can observe different energy values, U=m×g×h, corresponding to the same value of mass of the impacting steel sphere: these different values are obtained according to relationships by varying the falling height h and gravity acceleration g. These amplitudes are relative to the peak amplitude of the large A0 mode which is generated by the impact and has low frequency components, typically below 50 kHz as predicted by Ross in [23]. The data reported in Figure 3 are important to decide the programmable threshold value depending on the expected impact energy.…”

Section: Electronic Instrument Design For Shm Evaluationmentioning

confidence: 72%

Multichannel Real-Time Electronics Platform for the Estimation of the Error in Impact Localization With Different Piezoelectric Sensors Density

Capineri¹,

Bulletti²,

Merlo³

2021

Preprint

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The work presents a Structural Health Monitoring (SHM) electronic system with real-time ac-quisition and processing for the determination of impact location in laminates. The novelty of this work is the quantitative evaluation of impact location errors using the Lamb wave guided mode S0, captured and processed in real-time by up to eight piezoelectric sensors. The differential time of arrival is used to minimize an error function for the position estimation. The impact energy is correlated to the amplitudes of the antisymmetric (A0 ) mode and the electronic design is de-scribed to avoid saturation for signal acquisition. The same electronic is designed to acquire symmetric (S0 ) low level signals by adequate gain, bandwidth and signal to noise ration. Such signals propagate into a 1.4mm thick aluminum laminate at the group velocity of 5150m/s with frequency frequency components above 270kHz and can be discriminated from the A0 mode to calculate accurately the differential arrival time. The results show that the error is not improved better than S0 wavelength in impact localization by using six out of eight sensors connected to the electronic system.

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Section: Electronic Instrument Design For Shm Evaluationmentioning

confidence: 72%

Multichannel Real-Time Electronics Platform for the Estimation of the Error in Impact Localization With Different Piezoelectric Sensors Density

Capineri¹,

Bulletti²,

Merlo³

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Furthermore, in [ 22 ], the impacts are distinguished based on the potential energy of the impacting bodies, with values ranging from 500 mJ to 3.5 mJ. In other early studies on this subject [ 23 , 24 ], the impacts are instead distinguished based on the impact velocity. In several studies [ 24 , 25 , 26 ], signals that are generated by non-destructive impacts have been treated, which is, they do not cause any damage to the laminate under examination, neither with single impact nor with multiple impacts, however no information is given on the extent of impact.…”

Section: Characteristics Of Signals Generated By Impacts On Planar Structures Relevant To the Design Of Shm Systemsmentioning

confidence: 99%

“…In other early studies on this subject [ 23 , 24 ], the impacts are instead distinguished based on the impact velocity. In several studies [ 24 , 25 , 26 ], signals that are generated by non-destructive impacts have been treated, which is, they do not cause any damage to the laminate under examination, neither with single impact nor with multiple impacts, however no information is given on the extent of impact. Furthermore, in [ 27 ], the impacts are distinguished based on the potential energy of the impacting bodies, with values ranging from 500 mJ to 3.5 mJ.…”

Section: Characteristics Of Signals Generated By Impacts On Planar Structures Relevant To the Design Of Shm Systemsmentioning

confidence: 99%

“…The study of impacts that occur in an isotropic elastic flat plate is based on following assumptions: The ultrasonic signal that is generated by an impact is a guided wave signal that propagates into the plate without energy loss [ 24 , 30 ]. The frequency content of the ultrasonic signals that are generated by impacts depends on the impact velocity [ 28 , 31 ] and it is not modified during the propagation inside the plate [ 32 ].…”

Section: Characteristics Of Signals Generated By Impacts On Planar Structures Relevant To the Design Of Shm Systemsmentioning

confidence: 99%

“…The ultrasonic signal that is generated by an impact is a guided wave signal that propagates into the plate without energy loss [ 24 , 30 ].…”

Section: Characteristics Of Signals Generated By Impacts On Planar Structures Relevant To the Design Of Shm Systemsmentioning

confidence: 99%

See 2 more Smart Citations

Ultrasonic Guided-Waves Sensors and Integrated Structural Health Monitoring Systems for Impact Detection and Localization: A Review

Capineri

Bulletti

2021

Sensors

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This review article is focused on the analysis of the state of the art of sensors for guided ultrasonic waves for the detection and localization of impacts for structural health monitoring (SHM). The recent developments in sensor technologies are then reported and discussed through the many references in recent scientific literature. The physical phenomena that are related to impact event and the related main physical quantities are then introduced to discuss their importance in the development of the hardware and software components for SHM systems. An important aspect of the article is the description of the different ultrasonic sensor technologies that are currently present in the literature and what advantages and disadvantages they could bring in relation to the various phenomena investigated. In this context, the analysis of the front-end electronics is deepened, the type of data transmission both in terms of wired and wireless technology and of online and offline signal processing. The integration aspects of sensors for the creation of networks with autonomous nodes with the possibility of powering through energy harvesting devices and the embedded processing capacity is also studied. Finally, the emerging sector of processing techniques using deep learning and artificial intelligence concludes the review by indicating the potential for the detection and autonomous characterization of the impacts.

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Deep learning for impact detection in composite plates with sparsely integrated sensors

Damm

Spitzmüller

Raichle

et al. 2020

Smart Mater. Struct.

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In this paper, both location and energy of impacts on an anisotropic carbon fiber reinforced plate (CFRP) are detected with the help of deep learning. We introduce sparse low-cost sensor array integration in CFRP plates that allows for structural monitoring of lightweight structures. Using a resin transfer moulding process microelectromechanical systems (MEMS) and piezoelectric transducers (PZT) sensors are integrated into CFRP plates. We developed an automated test bench to perform weight drop impact loadings with impact energies ranging between 0.22–0.56 mJ on a 1 × 1 cm2-grid with 441 locations. The obtained sensor signals were processed by means of a short-time fourier transformation and used as input for the training of a deep learning model. This model was implemented with a convolutional neural network. To accelerate the training phase we introduce a coarse analytical model that generates artificial sensor signals we use for pretraining of the neural network. Yielding high prediction accuracies of 99.82% and 98.68% for a correct classification of impact location and energy, respectively, the capability of the proposed approach was demonstrated. Despite their limited resolution the low-cost MEMS accelerometers were able to correctly locate an impact and its energy with 99.76% and 97.04%, respectively. The pretraining led to an increased robustness of the training process. Additionally, for the case of PZT sensors, it also reduced the number of required epochs for convergence significantly.

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Structural Health Monitoring and Impact Detection Using Neural Networks for Damage Characterization

Cited by 6 publications

References 8 publications

Multichannel Real-Time Electronics Platform for the Estimation of the Error in Impact Localization With Different Piezoelectric Sensors Density

Multichannel Real-Time Electronics Platform for the Estimation of the Error in Impact Localization With Different Piezoelectric Sensors Density

Ultrasonic Guided-Waves Sensors and Integrated Structural Health Monitoring Systems for Impact Detection and Localization: A Review

Deep learning for impact detection in composite plates with sparsely integrated sensors

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