While guided wave structural health monitoring (SHM) is widely researched for ensuring safety, estimating performance deterioration, and detecting damage in structures, it experiences setbacks in accuracy due to varying environmental, sensor, and material factors. To combat these challenges, environmentally variable guided wave data is often stretched with temperature compensation methods, such as the scale transform and optimal signal stretch, to match a baseline signal and enable accurate damage detection. Yet, these methods fail for large environmental changes. This paper addresses this challenge by demonstrating a machine learning method to predict stretch factors. This is accomplished with feed-forward neural networks that approximate the complex velocity change function. We demonstrate that our machine learning approach outperforms the prior art on simulated Lamb wave data and is robust with extreme velocity variations. While our machine learning models do not conduct temperature compensation, their accurate stretch factor predictions serve as a proof of concept that a better model is plausible.
Impedance-based structural health monitoring (SHM) is recognized as a non-intrusive, highly sensitive, and model-independent SHM solution that is readily applicable to complex structures. This SHM method relies on analyzing the electromechanical impedance (EMI) signature of the structure under test over the time span of its operation. Changes in the EMI signature, compared to a baseline measured at the healthy state of the structure, often indicate damage. This method has successfully been applied to assess the integrity of numerous civil, aerospace, and mechanical components and structures. However, EMI sensitivity to environmental conditions, the temperature, in particular, has been an ongoing challenge facing the wide adoption of this method. Temperature-induced variation in EMI signatures can be misinterpreted as damage, leading to false positives, or may overshadow the effects of incipient damage in the structure.
In this paper, a new method for temperature compensation of EMI signature is presented. Data-driven dynamic models are first developed by fitting EMI signatures measured at various temperatures using the Vector Fitting algorithm. Once these models are developed, the dependence of model parameters on temperature is established. A parametric data-driven model is then derived with temperature as a parameter. This allows for EMI signatures to be calculated at any desired temperature. The capabilities of this new temperature compensation method are demonstrated on aluminum samples, where EMI signatures are measured at various temperatures. The developed method is found to be capable of temperature compensation of EMI signatures at a broad frequency range.
more » « less- Award ID(s):
- 1932213
- NSF-PAR ID:
- 10398921
- Date Published:
- Journal Name:
- Smart Materials, Adaptive Structures and Intelligent Systems
- Volume:
- 86274
- Page Range / eLocation ID:
- V001T05A009
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accurate decision-making requires understanding the factors that influence those decisions. In guided wave structural health monitoring, the first aim is to detect the presence of damage. This decision is based on the assumption that it is possible to discriminate between undamaged and damaged states. Sensing systems collect data to construct damage statistics, that is, numerical representations of how the state of the structure has changed over time. These damage statistics are designed to be sensitive to damage signatures in the data. However, environmental and operating conditions, such as the presence of temperature variations, perturb damage statistics and weaken decision-making. Temperature compensation strategies applied to reduce these perturbations are imperfect. Qualifying the performance of a temperature compensation strategy to reduce or eliminate the effects of temperature is dependent on the resulting damage statistics. Quality temperature compensation strategies must be able to recover damage statistics and increase damage detection accuracy. This article establishes a framework for evaluating the performance of damage detection methods that use temperature compensation to reduce the effects of environmental and operating conditions. Here, the scale transform and the dynamic time warping method are examined for data with varying temperature and signal-to-noise ratios. Monte Carlo simulations are used to construct probability densities and perform statistical analysis on the resulting damage statistics.
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Impedance-based non-destructive evaluation (NDE) constitutes a generalization of structural health monitoring (SHM), where comparisons between known-healthy reference structures and potentially-defective structures are used to identify damage. The quantity considered by impedance-based NDE is the electrical impedance of a piezoelectric element bonded to the part under test, which is linked to the dynamic vibrational response of the part under test through electromechanical coupling. In this work, the piezoelectric element is not bonded directly to the part under test, but rather to a c-shaped clamp, which is then mechanically attached to the part under test. Under this attachment condition, the effect of clamping force on the sensitivity of the impedance-based evaluation is investigated for machined steel blocks with varying levels of damage severity. The highest clamping force tested (600 lb, 2670 N) was the only condition exhibiting increasing damage metric values with increasing damage severity in the parts under test, suggesting that higher clamping force increases sensitivity to damage.more » « less
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