Delamination prediction in composite panels using unsupervised-feature learning methods with wavelet-enhanced guided wave representations
With the introduction of damage tolerance-based design philosophies, the demand for reliable and robust structural health monitoring (SHM) procedures for aerospace composite structures is increasing rapidly. The performance of supervised learning algorithms for SHM depends on the amount of labeled a...
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| Veröffentlicht in: | Composite structures Jg. 291; S. 115579 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier Ltd
01.07.2022
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| ISSN: | 0263-8223, 1879-1085 |
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| Abstract | With the introduction of damage tolerance-based design philosophies, the demand for reliable and robust structural health monitoring (SHM) procedures for aerospace composite structures is increasing rapidly. The performance of supervised learning algorithms for SHM depends on the amount of labeled and balanced datasets. Apart from this, collecting datasets accommodating all possible damage scenarios is cumbersome, costly, and inaccessible for aerospace applications. In this paper, we have proposed two different unsupervised-feature learning approaches where the algorithms are trained only on the baseline scenarios to learn the distribution of baseline signals. The trained unsupervised feature learner is used for delamination prediction with an anomaly detection philosophy. In the first approach, we have combined dimensionality reduction techniques (principal component analysis and independent component analysis) with a one-class support vector machine. In another approach, we have utilized deep learning-based deep convolutional autoencoders (CAE). These state-of-the-art algorithms are applied on three different guided wave-based experimental datasets. The raw guided wave signals present in the datasets are converted into wavelet-enhanced higher-order representations for training unsupervised feature-learning algorithms. We have also compared different techniques, and it is seen that CAE generates better reconstructions with lower mean squared error and can provide higher accuracy on all the datasets. |
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| AbstractList | With the introduction of damage tolerance-based design philosophies, the demand for reliable and robust structural health monitoring (SHM) procedures for aerospace composite structures is increasing rapidly. The performance of supervised learning algorithms for SHM depends on the amount of labeled and balanced datasets. Apart from this, collecting datasets accommodating all possible damage scenarios is cumbersome, costly, and inaccessible for aerospace applications. In this paper, we have proposed two different unsupervised-feature learning approaches where the algorithms are trained only on the baseline scenarios to learn the distribution of baseline signals. The trained unsupervised feature learner is used for delamination prediction with an anomaly detection philosophy. In the first approach, we have combined dimensionality reduction techniques (principal component analysis and independent component analysis) with a one-class support vector machine. In another approach, we have utilized deep learning-based deep convolutional autoencoders (CAE). These state-of-the-art algorithms are applied on three different guided wave-based experimental datasets. The raw guided wave signals present in the datasets are converted into wavelet-enhanced higher-order representations for training unsupervised feature-learning algorithms. We have also compared different techniques, and it is seen that CAE generates better reconstructions with lower mean squared error and can provide higher accuracy on all the datasets. |
| ArticleNumber | 115579 |
| Author | Gopalakrishnan, S. Rautela, Mahindra Senthilnath, J. Monaco, Ernesto |
| Author_xml | – sequence: 1 givenname: Mahindra orcidid: 0000-0002-2678-9682 surname: Rautela fullname: Rautela, Mahindra email: mrautela@iisc.ac.in organization: Department of Aerospace Engineering, Indian Institute of Science, Bangalore, India – sequence: 2 givenname: J. orcidid: 0000-0002-1737-7985 surname: Senthilnath fullname: Senthilnath, J. email: j_senthilnath@i2r.a-star.edu.sg organization: Institute for Infocomm Research, ASTAR, Singapore – sequence: 3 givenname: Ernesto surname: Monaco fullname: Monaco, Ernesto email: ermonaco@unina.it organization: Department of Industrial Engineering, University of Naples Federico II, Italy – sequence: 4 givenname: S. surname: Gopalakrishnan fullname: Gopalakrishnan, S. email: krishnan@iisc.ac.in organization: Department of Aerospace Engineering, Indian Institute of Science, Bangalore, India |
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| Cites_doi | 10.1002/stc.150 10.1117/1.OE.55.1.011007 10.1177/1475921717737971 10.1177/1475921720934051 10.1145/1390156.1390294 10.1023/A:1022627411411 10.1109/JSEN.2020.3009194 10.1177/1475921718817169 10.1088/1361-665X/ab58d6 10.1007/s42791-019-0012-2 10.1177/1475921704041876 10.1177/1475921714532989 10.1038/s41598-021-00326-2 10.1088/0964-1726/25/5/053001 10.1162/089976601750264965 10.1016/j.eswa.2020.114189 10.1016/j.aei.2020.101105 10.3390/aerospace5040111 10.1016/j.ultras.2021.106451 10.1177/1475921716639587 10.1088/0964-1726/22/12/125019 10.1016/S0893-6080(00)00026-5 10.1002/stc.2714 |
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| Keywords | Delamination detection Independent component analysis (ICA) One-class support vector machines (ocSVM) Principal component analysis (PCA) Convolutional autoencoders (CAE) |
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| SubjectTerms | Convolutional autoencoders (CAE) Delamination detection Independent component analysis (ICA) One-class support vector machines (ocSVM) Principal component analysis (PCA) |
| Title | Delamination prediction in composite panels using unsupervised-feature learning methods with wavelet-enhanced guided wave representations |
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