Szczegóły publikacji
Opis bibliograficzny
Diagnostic-quality guided wave signals synthesized using generative adversarial neural networks / Mateusz HEESCH, Michał Dziendzikowski, Krzysztof MENDROK, Ziemowit DWORAKOWSKI // Sensors [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 1424-8220. — 2022 — vol. 22 iss. 10 art. no. 3848, s. 1-16. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 14-16, Abstr. — Publikacja dostępna online od: 2022-05-19
Autorzy (4)
- AGHHeesch Mateusz
- Dziendzikowski Michał
- AGHMendrok Krzysztof
- AGHDworakowski Ziemowit
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 140840 |
|---|---|
| Data dodania do BaDAP | 2022-07-05 |
| Tekst źródłowy | URL |
| DOI | 10.3390/s22103848 |
| Rok publikacji | 2022 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Sensors |
Abstract
Guided waves are a potent tool in structural health monitoring, with promising machine learning algorithm applications due to the complexity of their signals. However, these algorithms usually require copious amounts of data to be trained. Collecting the correct amount and distribution of data is costly and time-consuming, and sometimes even borderline impossible due to the necessity of introducing damage to vital machinery to collect signals for various damaged scenarios. This data scarcity problem is not unique to guided waves or structural health monitoring, and has been partly addressed in the field of computer vision using generative adversarial neural networks. These networks generate synthetic data samples based on the distribution of the data they were trained on. Though there are multiple researched methods for simulating guided wave signals, the problem is not yet solved. This work presents a generative adversarial network architecture for guided waves generation and showcases its capabilities when working with a series of pitch-catch experiments from the OpenGuidedWaves database. The network correctly generates random signals and can accurately reconstruct signals it has not seen during training. The potential of synthetic data to be used for training other algorithms was confirmed in a simple damage detection scenario, with the classifiers trained exclusively on synthetic data and evaluated on real signals. As a side effect of the signal reconstruction process, the network can also compress the signals by 98.44% while retaining the damage index information they carry.