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EnglishThere have been many recent developments in the application of data-based methods to machine condition monitoring. A powerful methodology based on machine learning has emerged, where diagnostics are based on a two-step procedure: extraction of damage-sensitive features, followed by unsupervised learning (novelty detection) or supervised learning (classification). The objective of the current pair of papers is simply to illustrate one state-of-the-art procedure for each step, using synthetic data representative of reality in terms of size and complexity. The first paper in the pair will deal with feature extraction. Although some papers have appeared in the recent past considering stochastic resonance as a means of amplifying damage information in signals, they have largely relied on ad hoc specifications of the resonator used. In contrast, the current paper will adopt a principled optimisation-based approach to the resonator design. The paper will also show that a discrete dynamical system can provide all the benefits of a continuous system, but also provide a considerable speed-up in terms of simulation time in order to facilitate the optimisation approach.
An illustration of new methods in machine condition monitoring, Part I: Stochastic resonance / Worden, Keith; Antoniadou, I.; Marchesiello, Stefano; Mba, CLEMENT UCHECHUKWU; Garibaldi, Luigi. - In: JOURNAL OF PHYSICS. CONFERENCE SERIES. - ISSN 1742-6588. - ELETTRONICO. - 842(2017), pp. 1-10. ((Intervento presentato al convegno 12th International Conference on Damage Assessment of Structures, DAMAS 2017 tenutosi a Kitakyushu, Japan nel 10-12 July 2017.
| Titolo: | An illustration of new methods in machine condition monitoring, Part I: Stochastic resonance |
| Autori: | |
| Data di pubblicazione: | 2017 |
| Rivista: | |
| Abstract: | There have been many recent developments in the application of data-based methods to machine cond...ition monitoring. A powerful methodology based on machine learning has emerged, where diagnostics are based on a two-step procedure: extraction of damage-sensitive features, followed by unsupervised learning (novelty detection) or supervised learning (classification). The objective of the current pair of papers is simply to illustrate one state-of-the-art procedure for each step, using synthetic data representative of reality in terms of size and complexity. The first paper in the pair will deal with feature extraction. Although some papers have appeared in the recent past considering stochastic resonance as a means of amplifying damage information in signals, they have largely relied on ad hoc specifications of the resonator used. In contrast, the current paper will adopt a principled optimisation-based approach to the resonator design. The paper will also show that a discrete dynamical system can provide all the benefits of a continuous system, but also provide a considerable speed-up in terms of simulation time in order to facilitate the optimisation approach. |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| Worden_2017_J._Phys.__Conf._Ser._842_012058(1).pdf | Articolo principale | 2. Post-print / Author's Accepted Manuscript |  | Visibile a tuttiVisualizza/Apri |
http://hdl.handle.net/11583/2676291
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