MétaCan
Menu
Back to cohort
Record W2330912607 · doi:10.1115/imece2015-51719

High Sensitivity Damage Detection With Vibration Mode Shape Tuning Through the Optimal Design of Piezoelectric Actuators

2015· article· en· W2330912607 on OpenAlexaff
Shengjie Zhao, Nan Wu, Yukun Cheng

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsCantileverActuatorCurvatureSensitivity (control systems)PiezoelectricityVibrationBeam (structure)AcousticsVoltageNatural frequencyPiezoelectric sensorBendingMaterials scienceNormal modeStructural engineeringEngineeringElectronic engineeringPhysicsElectrical engineering

Abstract

fetched live from OpenAlex

This paper develops an advanced technique to significantly increase the frequency based damage detection sensitivity on a beam structure through a mode shape tuning process with the optimal design of the piezoelectric actuators. Piezoelectric sensors and actuators are mounted on the surface of the host beams to generate the feedback voltage and the active controlled excitations respectively. The excitations induced by the piezoelectric effect will be utilized to change the curvature distribution of the vibration mode shapes of the host beam structure so as to magnify the natural frequency difference between the intact beam and the damaged one to realize the high sensitivity damage detection. In this paper, theoretical models of the cantilever beams with and without mode shape tuning induced by piezoelectric layers are built first, while the damage is represented by a fixed end crack. Then, through the numerical simulations, the vibration mode shapes and corresponding natural frequencies of the beams can be solved to study the sensitivity improvement by using the proposed technique. In order to improve the detection efficiency, a couple of piezoelectric actuators are installed symmetrically on the upper and lower surface of the host beam, generating shifty bending moments to tune the vibration mode shapes. The actuation voltages applied on the actuators are determined by applying certain gain to the voltage from the piezoelectric sensors. Different gain factors are applied to the mode shape tuning process to reveal their effects on damage detection sensitivity improvement. As a result of the control process with proper gain factor, the curvature is more concentrated at the position close to the crack in the first vibration mode shape of the damaged beam comparing with the one without mode shape tuning. Therefore, the first natural frequency variation induced by the crack effect with mode shape tuning is much more significant than the one without mode shape tuning. In addition, further numerical simulations also indicate that the improvement of the detection sensitivity is closely related to the dimensions of the piezoelectric actuators. To realize better detection results, the optimal design of the size of the piezoelectric actuators is presented. The optimal length of the piezoelectric actuators is found leading to the best performance on damage detection. The theoretical studies and numerical simulations reflect that the proposed technique with mode shape tuning and optimally designed actuators is effective and promising in the field of damage detection. It is noted that the proposed technique can also be applied to improve the sensitivity of frequency based damage detection on other structures, e.g. plates and frames.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.000
Threshold uncertainty score0.001

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.030
GPT teacher head0.264
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2015
Admission routes1
Has abstractyes

Explore more

Same topicStructural Health Monitoring TechniquesFrench-language works237,207