Distributed Piezoelectric Sensor System for Damage Identification in Structures Subjected to Temperature Changes
Open accessAbstract
Structural health monitoring (SHM) is a very important area in a wide spectrum of fields and engineering applications. With an SHM system, it is possible to reduce the number of non-necessary inspection tasks, the associated risk and the maintenance cost in a wide range of structures during their lifetime. One of the problems in the detection and classification of damage are the constant changes in the operational and environmental conditions. Small changes of these conditions can be considered by the SHM system as damage even though the structure is healthy. Several applications for monitoring of structures have been developed and reported in the literature, and some of them include temperature compensation techniques. In real applications, however, digital processing technologies have proven their value by: (i) offering a very interesting way to acquire information from the structures under test; (ii) applying methodologies to provide a robust analysis; and (iii) performing a damage identification with a practical useful accuracy. This work shows the implementation of an SHM system based on the use of piezoelectric (PZT) sensors for inspecting a structure subjected to temperature changes. The methodology includes the use of multivariate analysis, sensor data fusion and machine learning approaches. The methodology is tested and evaluated with aluminum and composite structures that are subjected to temperature variations. Results show that damage can be detected and classified in all of the cases in spite of the temperature changes.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Temperature is a potential confounding factor for SHM systems, especially large infrastructures, because temperature-induced variations in measured signals can have magnitudes similar to those generated by damages.
“temperature is well known to be a potential confounding factor for SHM systems, especially when applied to large infrastructures, due to the variations induced on the measured signals, whose magnitudes, under certain circumstances, may be similar to those generated by damages.”
From Vibration-based structural health monitoring exploiting a combination of convolutional neural networks and autoencoders for temperature effects neutralization (2022) · cites this paper as [21]
The Experimental Setup and Results section reports temperature-induced changes in measured signal phase and amplitude, and the Concluding Remarks say these changes can cause false-positive damage detection in healthy structures. The copy does not establish that the signal variations are similar in magnitude to those caused by damage, or that the problem is especially pronounced in large infrastructure. (LLM, conf=0.84)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439 · see citing works
Nearest neighbor pattern classification
Cover, T., & Hart, P. (1967) · IEEE Transactions on Information Theory · doi:10.1109/tit.1967.1053964 · Cited by 16,936 · see citing works
Structural Health Monitoring: A Machine Learning Perspective
Farrar, C., & Worden, K. (2012) · Wiley eBooks · doi:10.1002/9781118443118 · Cited by 1,605 · see citing works
Data Mining and Knowledge Discovery Handbook
Maimon, O., & Rokach, L. (2005) · Springer eBooks · doi:10.1007/b107408 · Cited by 1,559 · see citing works
Effects of environmental and operational variability on structural health monitoring
Sohn, H. (2007) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2006.1935 · Cited by 883 · see citing works
Structural health monitoring for a wind turbine system: a review of damage detection methods
Ciang, C. C., Lee, J., & Bang, H. (2008) · Measurement Science and Technology · doi:10.1088/0957-0233/19/12/122001 · Cited by 807 · see citing works
Classification and Regression Trees, Bagging, and Boosting
Sutton, C. D. (2005) · Handbook of statistics · doi:10.1016/s0169-7161(04)24011-1 · Cited by 603 · see citing works
Efficient kNN classification algorithm for big data
Deng, Z., Zhu, X., Cheng, D., Zong, M., & Zhang, S. (2016) · Neurocomputing · doi:10.1016/j.neucom.2015.08.112 · Cited by 603 · see citing works
Deraemaeker, A., Reynders, E. P. B., Roeck, G. D., & Kullaa, J. (2007) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2007.07.004 · Cited by 584 · see citing works
Structural damage diagnosis under varying environmental conditions—Part I: A linear analysis
Yan, A.-M., Kerschen, G., Boe, P. D., & Golinval, J. (2005) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2004.12.002 · Cited by 537 · see citing works
Machine learning algorithms for damage detection under operational and environmental variability
Figueiredo, E., Park, G., Farrar, C. R., Keith Worden, B., & Figueiras, J. A. (2010) · Structural Health Monitoring · doi:10.1177/1475921710388971 · Cited by 444 · see citing works
The application of machine learning to structural health monitoring
Keith Worden, B., & Manson, G. (2006) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2006.1938 · Cited by 436 · see citing works
Statistical Damage Classification Under Changing Environmental and Operational Conditions
Sohn, H., Worden, K., & Farrar, C. R. (2002) · Journal of Intelligent Material Systems and Structures · doi:10.1106/104538902030904 · Cited by 295 · see citing works
Baptista, F. G., Budoya, D. E., de Almeida, V. A. D., & Ulson, J. A. C. (2014) · Sensors · doi:10.3390/s140101208 · Cited by 287 · see citing works
Keith Worden, B., Manson, G., & ALLMAN, D. (2002) · Journal of Sound and Vibration · doi:10.1006/jsvi.2002.5168 · Cited by 258 · see citing works
Q-statistic and T2-statistic PCA-based measures for damage assessment in structures
Delgado, L. E. M., Rodellar, J., Fernández, A., & Güemes, A. (2010) · Structural Health Monitoring · doi:10.1177/1475921710388972 · Cited by 240 · see citing works
iPCA: An Interactive System for PCA‐based Visual Analytics
Jeong, D. H., Ziemkiewicz, C., Fisher, B., Ribarsky, W., & Chang, R. (2009) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2009.01475.x · Cited by 223 · see citing works
Temperature effects in ultrasonic Lamb wave structural health monitoring systems
Lanza di Scalea, F., & Salamone, S. (2008) · The Journal of the Acoustical Society of America · doi:10.1121/1.2932071 · Cited by 203 · see citing works
Manson, G., Worden, K., & ALLMAN, D. (2002) · Journal of Sound and Vibration · doi:10.1006/jsvi.2002.5167 · Cited by 150 · see citing works
Vitola, J., Pozo, F., Burgos, D. A. T., & Anaya, M. (2017) · Sensors · doi:10.3390/s17020417 · Cited by 147 · see citing works
Self-citationLeichtle, T., Geiß, C. M., Wurm, M., et al. (2016) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2016.08.010 · Cited by 142 · see citing works
Burgos, D. A. T., Delgado, L. E. M., & Rodellar, J. (2012) · Structural Control and Health Monitoring · doi:10.1002/stc.1540 · Cited by 133 · see citing works
Weak and Electromagnetic Interactions
Guidry, M. (1991) · doi:10.1002/9783527617357.ch6 · Cited by 94 · see citing works
Vanlanduit, S., Parloo, E., Cauberghe, B., Guillaume, P., & Verboven, P. (2004) · Journal of Sound and Vibration · doi:10.1016/j.jsv.2004.07.016 · Cited by 92 · see citing works
A Review of various k-Nearest Neighbor Query Processing Techniques
Dhanabal, S., & Chandramathi, S. (2011) · Cited by 89 · see citing works
Pseudo-degrees of freedom for complex predictive models: the example of partial least squares
van der Voet, H. (1999) · Journal of Chemometrics · doi:10.1002/(sici)1099-128x(199905/08)13:3/4 · Cited by 86 · see citing works
Merging Sensor Data from Multiple Temperature Scenarios for Vibration Monitoring of Civil Structures
Balmès, É., Basseville, M., Bourquin, F., Mevel, L., Nasser, H., & Treyssède, F. (2008) · Structural Health Monitoring · doi:10.1177/1475921708089823 · Cited by 75 · see citing works
Delgado, L. E. M., Ordóñez, M. R., Pozo, F., et al. (2013) · Smart Materials and Structures · doi:10.1088/0964-1726/23/2/025014 · Cited by 74 · see citing works
Self-citationImprovement of Search Strategy With K-Nearest Neighbors Approach for Traffic State Prediction
Oh, S., Byon, Y.-J., & Yeo, H. (2015) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2015.2498408 · Cited by 72 · see citing works
Adhesive Layer Effects on PZT-induced Lamb Waves at Elevated Temperatures
Ha, S.-W., Lonkar, K., Mittal, A., et al. (2010) · Structural Health Monitoring · doi:10.1177/1475921710365267 · Cited by 71 · see citing works
Lamb wave–based quantitative crack detection using a focusing array algorithm
Yu, L., & Leckey, C. A. C. (2012) · Journal of Intelligent Material Systems and Structures · doi:10.1177/1045389x12469452 · Cited by 68 · see citing works
Wind Turbine Fault Detection through Principal Component Analysis and Statistical Hypothesis Testing
Pozo, F., & Vidal, Y. (2015) · Energies · doi:10.3390/en9010003 · Cited by 68 · see citing works
Self-citationObserver and Data-Driven-Model-Based Fault Detection in Power Plant Coal Mills
Odgaard, P. F., Lin, B., & Jørgensen, S. B. (2008) · IEEE Transactions on Energy Conversion · doi:10.1109/tec.2007.914185 · Cited by 57 · see citing works
Fault Detection Using the Clustering-kNN Rule for Gas Sensor Arrays
Yang, J., Sun, Z., & Chen, Y. (2016) · Sensors · doi:10.3390/s16122069 · Cited by 54 · see citing works
Pozo, F., Arruga, I., Delgado, L. E. M., Ordóñez, M. R., & Podivilova, E. (2016) · Structural Health Monitoring · doi:10.1177/1475921715624504 · Cited by 39 · see citing works
Self-citationData-driven methodology to detect and classify structural changes under temperature variations
Anaya, M., Burgos, D. A. T., Torres-Arredondo, M. Á., et al. (2014) · Smart Materials and Structures · doi:10.1088/0964-1726/23/4/045006 · Cited by 33 · see citing works
Self-citationAnaya, M., Burgos, D. A. T., & Pozo, F. (2015) · Shock and Vibration · doi:10.1155/2015/648097 · Cited by 32 · see citing works
Self-citationTorres-Arredondo, M.-A., Burgos, D. A. T., McGugan, M., et al. (2013) · Smart Materials and Structures · doi:10.1088/0964-1726/22/10/105023 · Cited by 24 · see citing works
Design and validation of a structural health monitoring system for aeronautical structures.
Burgos, T., & Alexánder, D. (2013) · TDX (Tesis Doctorals en Xarxa) · Cited by 22 · see citing works
Anaya, M., Burgos, D. A. T., & Pozo, F. (2017) · International Journal of Bio-Inspired Computation · doi:10.1504/ijbic.2017.081843 · Cited by 18 · see citing works
Self-citationComparison of several methods for damage localization using indices and contributions based on PCA
Burgos, D. A. T., Delgado, L. E. M., & Rodellar, J. (2011) · Journal of Physics Conference Series · doi:10.1088/1742-6596/305/1/012013 · Cited by 17 · see citing works
Torres-Arredondo, M. Á., Mueller, I., Burgos, D. A. T., et al. (2013) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-013-0060-5 · Cited by 16 · see citing works
Torres-Arredondo, M. Á., Sierra-Pérez, J., & Cabanès, G. (2016) · Smart Materials and Structures · doi:10.1088/0964-1726/25/5/055034 · Cited by 16 · see citing works
Guaranteeing robustness of structural condition monitoring to environmental variability
Buren, K. V., Reilly, J., Neal, K. D., et al. (2016) · Journal of Sound and Vibration · doi:10.1016/j.jsv.2016.08.038 · Cited by 16 · see citing works
A novel machine-learning approach for structural state identification using ultrasonic guided waves
Roy, S., Chang, F., Lee, S., et al. (2014) · CRC Press eBooks · doi:10.1201/b16387-48 · Cited by 15 · see citing works
Burgos, D. A. T., Anaya, M., Forero, E., et al. (2016) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/138/1/012016 · Cited by 9 · see citing works
Self-citationAdaptive learning for damage classification in structural health monitoring
Chakraborty, D., Kovvali, N., Zhang, J. J., et al. (2009) · 2018 52nd Asilomar Conference on Signals, Systems, and Computers · doi:10.1109/acssc.2009.5469782 · Cited by 4 · see citing works
System for Structural Health Monitoring based on piezoelectric sensors/actuators
Neto, R. M. F., Steffen, V., Rade, D. A., et al. (2011) · doi:10.1109/cobep.2011.6085180 · Cited by 2 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- [10]Anaya M. Tibaduiza D. Pozo F. Artificial Immune System (AIS) for Damage Detection Under Variable Temperature Conditions Proceedings of the European Workshop on Structural Health Monitoring Bilbao, Spain 5–8 July 2016
- [21]Tibaduiza D. Mujica L. Anaya M. Rodellar J. Güemes A. Independent component analysis for detecting damages on aircraft wing skeleton Proceedings of the 5th European Conference on Structural Control Genoa, Italy 18–20 June 2012
- [24]Jolliffe I. Principal Component Analysis Wiley Online Library New York, NY, USA 2002
Cited by
Sorted by citations
Parziale, M., Lomazzi, L., Giglio, M., & Cadini, F. (2022) · Structural Control and Health Monitoring · doi:10.1002/stc.3076 · Cited by 22 · see citing works
- Authors
- Vitola, J., Pozo, F., Tibaduiza, D., & Anaya, M.
- Affiliations
- Universidad Santo Tomás (CO)
- Universitat Politècnica de Catalunya (ES)
- Departamento de Ingeniería Eléctrica y Electrónica, Universidad Nacional de Colombia, Cra 45 No. 26-85, Bogotá 111321, Colombia
- Fundación Universitaria Los Libertadores (CO)
- Corresponding author
- Pozo, Francesc
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2017-05-31
- Volume
- 17
- Issue
- 6
- Pages
- 1252
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC5492726
- OpenAlex
- W2619030265
- Cited by
- 60
- References
- 48
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 5 · 2023: 9 · 2022: 7 · 2021: 6 · 2020: 13 · 2019: 8 · 2018: 7 · 2017: 1
- FWCI
- 5.73
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (acceptedVersion, cc-by-nc-nd)
- UPCommons institutional repository (Universitat Politècnica de Catalunya) (submittedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- RECERCAT (Consorci de Serveis Universitaris de Catalunya) (submittedVersion, cc-by-nc-nd)
- Repositorio Institucional FULL (submittedVersion)
- Funders
- Ministerio de Ciencia e Innovación (DPI2014-58427-C2-1-R)
- Generalitat de Catalunya (2014 SGR 859)
- FODEIN 2017 (17545020-47)
- Topics
- Physical Sciences › Engineering › Mechanics of Materials › Ultrasonics and Acoustic Wave Propagation
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
- Physical Sciences › Engineering › Mechanical Engineering › Non-Destructive Testing Techniques
- Keywords
- structural health monitoring, piezoelectric sensors, PZT sensors, damage detection, damage classification, temperature compensation, temperature change, sensor fusion, multivariate analysis, machine learning, aluminium structures, composite structures, distributed sensor systems
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
