Compressed sensing techniques for detecting damage in structures

Mascareñas, David; Cattaneo, Alessandro; Theiler, James; Farrar, Charles R.

doi:10.1177/1475921713486164

Cited by 72 publications

(51 citation statements)

References 21 publications

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“…Sparsity is the key assumption that underlies the framework of CS. A few prior applications of CS in the field of SHM have been reported to date [30], [31], [32], [33], [34]. Particularly, Bao et al [31] have investigated the potential of CS for the compression of vibration data obtained from bridge SHM systems.…”

Section: Introductionmentioning

confidence: 99%

Embedding Compressive Sensing-Based Data Loss Recovery Algorithm Into Wireless Smart Sensors for Structural Health Monitoring

Zou

Bao

et al. 2015

IEEE Sensors J.

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Lossy transmission is a common problem for monitoring systems based on wireless sensors. Reliable communication protocols, which enhance communication reliability by repetitively transmitting unreceived packets, is one approach to tackle the problem of data loss. An alternative approach allows data loss to some extent and seeks to recover the lost data from an algorithmic point of view. Compressive sensing (CS) provides such a data loss recovery technique. This technique can be embedded into smart wireless sensors and effectively increases wireless communication reliability without re-transmitting the data; the promise of this approach is to reduce communication and thus power savings. The basic idea of CS-based approach is that, instead of transmitting the raw signal acquired by the sensor, a transformed signal that is generated by projecting the raw signal onto a random matrix, is transmitted. Some data loss may occur during the transmission of this transformed signal. However, according to the theory of CS, the raw signal can be effectively reconstructed from the received incomplete transformed signal given that the raw signal is compressible in some basis and the data loss ratio is low. Specifically, this research targets to provide accurate compensation for stationary and compressible acceleration signals obtained from Structural Health Monitoring (SHM) systems with data loss ratio below 20%. This CS-based technique is implemented into the Imote2 smart sensor platform using the foundation of Illinois Structural Health Monitoring Project (ISHMP) Service Tool-suite. To overcome the constraints of limited onboard resources of wireless sensor nodes, a method called random demodulator (RD) is employed to provide memory and power efficient construction of the random sampling matrix. Adaptation of RD sampling matrix is made to accommodate data loss in wireless transmission and meet the objectives of the data recovery. The embedded program is tested in a series of sensing and communication experiments. Examples and parametric study are presented to demonstrate the applicability of the embedded program as well as to show the efficacy of CS-based data loss recovery for real wireless SHM systems.

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Section: Introductionmentioning

confidence: 99%

Embedding Compressive Sensing-Based Data Loss Recovery Algorithm Into Wireless Smart Sensors for Structural Health Monitoring

Zou

Bao

et al. 2015

IEEE Sensors J.

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“…Therefore, in order to achieve long battery lifetime, performing data reduction locally, i.e. within the wireless smart sensors, is of primary importance [9,10]. By doing so communication traffic can be greatly reduced, minimizing the need of storing or transmitting large amount of multichannel data.…”

Section: Introductionmentioning

confidence: 99%

Best basis compressive sensing of guided waves in structural health monitoring

Perelli

Marchi

Flamigni

et al. 2015

Digital Signal Processing

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“…D. Macarena built a compressed sensor node and collected compress measurements of acceleration by the sensor node from a 3-story structure model. He recovers the signal with the Basis Pursuit (BP) algorithm and increase the signals recover precision with redundant dictionary [4] . Sparsity of signal is the basic premise to CS.…”

Section: Introductionmentioning

confidence: 99%

Compressed sensing techniques for arbitrary frequency-sparse signals in structural health monitoring

Duan

Kang

2014

SPIE Proceedings

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Structural health monitoring requires collection of large number sample data and sometimes high frequent vibration data for detecting the damage of structures. The expensive cost for collecting the data is a big challenge. The recent proposed Compressive Sensing method enables a potentially large reduction in the sampling, and it is a way to meet the challenge. The Compressed Sensing theory requires sparse signal, meaning that the signals can be well-approximated as a linear combination of just a few elements from a known discrete basis or dictionary. The signal of structure vibration can be decomposed into a few sinusoid linear combinations in the DFT domain. Unfortunately, in most cases, the frequencies of decomposed sinusoid are arbitrary in that domain, which may not lie precisely on the discrete DFT basis or dictionary. In this case, the signal will lost its sparsity, and that makes recovery performance degrades significantly. One way to improve the sparsity of the signal is to increase the size of the dictionary, but there exists a tradeoff: the closely-spaced DFT dictionary will increase the coherence between the elements in the dictionary, which in turn decreases recovery performance.In this work we introduce three approaches for arbitrary frequency signals recovery. The first approach is the continuous basis pursuit (CBP), which reconstructs a continuous basis by introducing interpolation steps. The second approach is a semidefinite programming (SDP), which searches the sparest signal on continuous basis without establish any dictionary, enabling a very high recovery precision. The third approach is spectral iterative hard threshold (SIHT), which is based on redundant DFT dictionary and a restricted union-of-subspaces signal model, inhibiting closely spaced sinusoids. The three approaches are studied by numerical simulation. Structure vibration signal is simulated by a finite element model, and compressed measurements of the signal are taken to perform signal recovery. Comparison of the performance of the three approaches is made, and future work on design of compressive sampling testing system for vibration signal is proposed.

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Compressed sensing techniques for detecting damage in structures

Cited by 72 publications

References 21 publications

Embedding Compressive Sensing-Based Data Loss Recovery Algorithm Into Wireless Smart Sensors for Structural Health Monitoring

Embedding Compressive Sensing-Based Data Loss Recovery Algorithm Into Wireless Smart Sensors for Structural Health Monitoring

Best basis compressive sensing of guided waves in structural health monitoring

Compressed sensing techniques for arbitrary frequency-sparse signals in structural health monitoring

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