A Data Loss Recovery Technique using Compressive Sensing for Structural Health Monitoring Applications

Thadikemalla, Venkata Sainath Gupta; Gandhi, A. S.

doi:10.1007/s12205-017-2070-z

Cited by 22 publications

(5 citation statements)

References 24 publications

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“…Vibration signals with low damping factors are sparse in the Fourier domain [24]. For these reasons, the Discrete Cosine Transform (DCT) or the Discrete Fourier Transform (DFT) matrices have been conventionally chosen as sparsifying bases [10], [25]. Alternatively, Wavelet bases have been proposed [26] to better track non-stationary phenomena.…”

Section: B Structurally-shaped Sparsity Basismentioning

confidence: 99%

Model-Assisted Compressed Sensing for Vibration-Based Structural Health Monitoring

Zonzini

Zauli

Mangia

et al. 2021

IEEE Trans. Ind. Inf.

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The main challenge in the implementation of longlasting vibration monitoring systems is to tackle the constantly evolving complexity of modern 'mesoscale' structures. Thus, the design of energy aware solutions is promoted for the joint optimization of data sampling rates, on-board storage requirements, and communication data payloads.In this context, the present work explores the feasibility of the rakeness-based compressed sensing (Rak-CS) approach to tune the sensing mechanism on the second-order statistics of measured data. In particular, a novel model-assisted variant (MRak-CS) is proposed, which is built on a synthetic derivation of the spectral profile of the structure by pivoting on numerical priors. Moreover, a signal-adapted sparsity basis relying on the Wavelet Packet Transform operator is conceived, which aims at maximizing the signal sparsity while allowing for a precise timefrequency localization.The adopted solutions were tested with experiments performed on a sensorized pinned-pinned steel beam. Results prove that the rakeness-based compression strategies are superior to conventional eigenvalue approaches and to standard CS methods. The achieved compression ratio is equal to 7 and the quality of the reconstructed structural parameters is preserved even in presence of defective configurations.

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Section: B Structurally-shaped Sparsity Basismentioning

confidence: 99%

Model-Assisted Compressed Sensing for Vibration-Based Structural Health Monitoring

Zonzini

Zauli

Mangia

et al. 2021

IEEE Trans. Ind. Inf.

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“…The CS-based method has been improved to recover lost data combined with machine learning techniques [25]. In CS, the signal is encoded using scrambled identity matrix to reduce the computational complexity [26]. A modified two-task learning algorithm has been used to enhance the accuracy of CS signal reconstruction [27].…”

Section: Introductionmentioning

confidence: 99%

Full-field static and dynamic strain measurement by an inverse conjugate beam method with two-type sensor placement

Zhang,

Li,

Tian

et al. 2024

Meas. Sci. Technol.

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This paper presents a novel inverse conjugate beam method (ICBM) to full-field static and dynamic strain measurement using long-gauge fiber Bragg grating (FBG) and vision sensors. By applying reverse analysis of conjugate beam theory, the ICBM establishes a displacement-strain transformation model that effectively explains the correlation between displacement, distributed strain and desired strain. The static and dynamic strain can be reconstructed directly by combining the monitored displacement and strain responses at the displacement monitoring locations. The vision sensor is employed to complement the installed long-gauge strain sensor for monitoring the displacement of the location without a long-gauge sensor. This method helps overcome the difficulty of monitoring the full-bridge strain due to insufficient sensors or inaccessible monitoring positions. At the same time, according to this method, it is necessary to use the displacement from the visual sensor to determine the residual stiffness of each unit as prior information for the ICBM. Both numerical studies and laboratory tests are carried out on a simply supported beam for conceptual verification. The results demonstrate that the proposed ICBM successfully achieves static and dynamic strain response reconstruction at displacement monitoring locations.

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“…The measurement matrix (MM), which is the core content of CS, plays an important role in signal acquisition and reconstruction [25,26]. Currently, measurement matrices that satisfy the restricted isometry property (RIP) [27] are mainly divided into two categories: are deterministic and randomness matrices.…”

Section: Introductionmentioning

confidence: 99%

A novel method to repair missing vibration data in rolling bearing vibration signals based on improved optimized measurement matrix

Wang

Liu

et al. 2023

Meas. Sci. Technol.

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Missing data caused by many factors such as equipment short circuits or data cleaning affect the accuracy of rotating machinery condition monitoring. To improve the precision of missing data recovery, a compressed sensing (CS)-based vibration data repair method is developed. Firstly, based on the Gaussian random matrix, an improved optimized measurement matrix (OMM) is proposed to accurately sample data. Then a sparse representation of the vibration signal through discrete cosine transform (DCT) is utilized to sparse the noisy vibration signal. Finally, the orthogonal matching pursuit (OMP) algorithm is employed to reconstruct the missing signal. The effectiveness of the proposed method is verified by analyzing constant and variable speed time series of rolling bearings. Compared with other data repair methods, it is highlighted that OMM has a higher repair precision at different loss rates.

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A Data Loss Recovery Technique using Compressive Sensing for Structural Health Monitoring Applications

Cited by 22 publications

References 24 publications

Model-Assisted Compressed Sensing for Vibration-Based Structural Health Monitoring

Model-Assisted Compressed Sensing for Vibration-Based Structural Health Monitoring

Full-field static and dynamic strain measurement by an inverse conjugate beam method with two-type sensor placement

A novel method to repair missing vibration data in rolling bearing vibration signals based on improved optimized measurement matrix

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