Time-Inferred Autoencoder: A noise adaptive condition monitoring tool

Kulkarni, Nitin Nagesh; Valente, Nicholas A.; Sabato, Alessandro

doi:10.1016/j.ymssp.2023.110789

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2024

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Cited by 3 publications

References 60 publications

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Time-Inferred Sparse Autoencoder for Improved Full-Field Reconstruction from Sparse Measurements

Kulkarni,

Sabato

2024

Conference Proceedings of the Society for Experimental Mechanics Series

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Time-Inferred Sparse Autoencoder for Improved Full-Field Reconstruction from Sparse Measurements

Kulkarni,

Sabato

2024

Conference Proceedings of the Society for Experimental Mechanics Series

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Research on online identification of surface burnishing tool machining conditions by spindle current signal analysis

Zhong-yu,

Chao-tang,

Zhi-peng

et al. 2024

Tribology International

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Parametric study on the accuracy of full-field reconstruction from sparse measurements using autoencoders

Kulkarni,

Sabato

2024

Health Monitoring of Structural and Biological Systems XVIII

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Full-field data provides a comprehensive understanding of the behavior of a system or structure, which is particularly crucial when identifying local damages. These damage may exhibit complex and subtle effects that could be overlooked with sparse measurements. Recent advancements in machine learning, such as autoencoders (AE), have enabled the reconstruction of full-field data using sparse measurements. However, a study assessing the accuracy of AE in reconstructing full-field data concerning measurement locations, data sparsity, and noise density is still lacking in the context of nondestructive evaluation (NDE). To address these gaps, this study adopts a parametric approach to evaluate the effectiveness of an LSTM-based AE model in terms of measurement locations, data sparsity, and noise density. The two sets of data (i.e., configuration #1 and #2) were generated using a finite element method for a 2D metallic plate cooling. The configuration #1 data were then used to train the LSTM-based AE and the model's full field reconstruction performance was validated on sparse measurements of configuration #2 using the average reconstruction error (ARE) as a testing parameter. The result shows, there was no significant impact of different measurement locations on ARE. Whereas ARE increased with increase in data sparsity and noise density. This research presents a parametric study with potential applications in full-field reconstruction, not limited to thermal data. It can be extended to other applications, such as strain, displacement, and velocity, in scenarios where the targeted system undergoes temporal evolution.

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Time-Inferred Autoencoder: A noise adaptive condition monitoring tool

Cited by 3 publications

References 60 publications

Time-Inferred Sparse Autoencoder for Improved Full-Field Reconstruction from Sparse Measurements

Time-Inferred Sparse Autoencoder for Improved Full-Field Reconstruction from Sparse Measurements

Research on online identification of surface burnishing tool machining conditions by spindle current signal analysis

Parametric study on the accuracy of full-field reconstruction from sparse measurements using autoencoders

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