Stochastic Identification of Guided Wave Propagation under Ambient Temperature via Non-Stationary Time Series Models
In the context of active-sensing guided-wave-based acousto-ultrasound structural health monitoring, environmental and operational variability poses a considerable challenge in the damage diagnosis process as they may mask the presence of damage. In this work, the stochastic nature of guided wave pro...
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MDPI AG
2021-08-01
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| Online Access: | https://www.mdpi.com/1424-8220/21/16/5672 |
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| author | Shabbir Ahmed Fotis Kopsaftopoulos |
| author_facet | Shabbir Ahmed Fotis Kopsaftopoulos |
| author_sort | Shabbir Ahmed |
| collection | DOAJ |
| description | In the context of active-sensing guided-wave-based acousto-ultrasound structural health monitoring, environmental and operational variability poses a considerable challenge in the damage diagnosis process as they may mask the presence of damage. In this work, the stochastic nature of guided wave propagation due to the small temperature variation, naturally occurring in the ambient or environment, is rigorously investigated and modeled with the help of stochastic time-varying time series models, for the first time, with a system identification point of view. More specifically, the output-only recursive maximum likelihood time-varying auto-regressive model (RML-TAR) is employed to investigate the uncertainty in guided wave propagation by analyzing the time-varying model parameters. The steps and facets of the identification procedure are presented, and the obtained model is used for modeling the uncertainty of the time-varying model parameters that capture the underlying dynamics of the guided waves. The stochasticity inherent in the modal properties of the system, such as natural frequencies and damping ratios, is also analyzed with the help of the identified RML-TAR model. It is stressed that the narrow-band high-frequency actuation for guided wave propagation excites more than one frequency in the system. The values and the time evolution of those frequencies are analyzed, and the associated uncertainties are also investigated. In addition, a high-fidelity finite element (FE) model was established and Monte Carlo simulations on that FE model were carried out to understand the effect of small temperature perturbation on guided wave signals. |
| first_indexed | 2024-03-10T08:23:03Z |
| format | Article |
| id | doaj.art-045ba5e8cbff4f6cbea69a9eb499d131 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T08:23:03Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj.art-045ba5e8cbff4f6cbea69a9eb499d1312023-11-22T09:43:52ZengMDPI AGSensors1424-82202021-08-012116567210.3390/s21165672Stochastic Identification of Guided Wave Propagation under Ambient Temperature via Non-Stationary Time Series ModelsShabbir Ahmed0Fotis Kopsaftopoulos1Intelligent Structural Systems Laboratory (ISSL), Department of Mechanical, Aerospace and Nuclear Engieering, Rensselaer Polytechnic Institute, Troy, NY 12180, USAIntelligent Structural Systems Laboratory (ISSL), Department of Mechanical, Aerospace and Nuclear Engieering, Rensselaer Polytechnic Institute, Troy, NY 12180, USAIn the context of active-sensing guided-wave-based acousto-ultrasound structural health monitoring, environmental and operational variability poses a considerable challenge in the damage diagnosis process as they may mask the presence of damage. In this work, the stochastic nature of guided wave propagation due to the small temperature variation, naturally occurring in the ambient or environment, is rigorously investigated and modeled with the help of stochastic time-varying time series models, for the first time, with a system identification point of view. More specifically, the output-only recursive maximum likelihood time-varying auto-regressive model (RML-TAR) is employed to investigate the uncertainty in guided wave propagation by analyzing the time-varying model parameters. The steps and facets of the identification procedure are presented, and the obtained model is used for modeling the uncertainty of the time-varying model parameters that capture the underlying dynamics of the guided waves. The stochasticity inherent in the modal properties of the system, such as natural frequencies and damping ratios, is also analyzed with the help of the identified RML-TAR model. It is stressed that the narrow-band high-frequency actuation for guided wave propagation excites more than one frequency in the system. The values and the time evolution of those frequencies are analyzed, and the associated uncertainties are also investigated. In addition, a high-fidelity finite element (FE) model was established and Monte Carlo simulations on that FE model were carried out to understand the effect of small temperature perturbation on guided wave signals.https://www.mdpi.com/1424-8220/21/16/5672guided wavesstochastic identificationtime-varying modelspiezoelectric sensorstime series modelsnon-stationary models |
| spellingShingle | Shabbir Ahmed Fotis Kopsaftopoulos Stochastic Identification of Guided Wave Propagation under Ambient Temperature via Non-Stationary Time Series Models Sensors guided waves stochastic identification time-varying models piezoelectric sensors time series models non-stationary models |
| title | Stochastic Identification of Guided Wave Propagation under Ambient Temperature via Non-Stationary Time Series Models |
| title_full | Stochastic Identification of Guided Wave Propagation under Ambient Temperature via Non-Stationary Time Series Models |
| title_fullStr | Stochastic Identification of Guided Wave Propagation under Ambient Temperature via Non-Stationary Time Series Models |
| title_full_unstemmed | Stochastic Identification of Guided Wave Propagation under Ambient Temperature via Non-Stationary Time Series Models |
| title_short | Stochastic Identification of Guided Wave Propagation under Ambient Temperature via Non-Stationary Time Series Models |
| title_sort | stochastic identification of guided wave propagation under ambient temperature via non stationary time series models |
| topic | guided waves stochastic identification time-varying models piezoelectric sensors time series models non-stationary models |
| url | https://www.mdpi.com/1424-8220/21/16/5672 |
| work_keys_str_mv | AT shabbirahmed stochasticidentificationofguidedwavepropagationunderambienttemperaturevianonstationarytimeseriesmodels AT fotiskopsaftopoulos stochasticidentificationofguidedwavepropagationunderambienttemperaturevianonstationarytimeseriesmodels |