Vibration monitoring via spectro-temporal compressive sensing for wireless sensor networks

Klis, Roman; Chatzi, Eleni

doi:10.1201/9781351204590-17

Cited by 7 publications

(9 citation statements)

References 1 publication

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“…MAC ≥ 0.9) when CR is increased up to CR = 7, which guarantees a sufficient modal consistency for all the inspected modes. This result compares favorably with alternative solutions presented in the literature, where the estimated CR values were consistently below CR = 5 [9].…”

Section: B Compression Approach: Rakness-based Vs Benchmark Solutionssupporting

confidence: 80%

“…In the last few years, many researchers investigated the suitability of different CS techniques in the vibration-based SHM context. Among the most representative examples, authors in [9] explored the advantages of a combined spectrotemporal compression approach (STCS) leveraging a bidirectional feature transmission scheme. A distributed CS paradigm was proposed, which involves several leaf nodes under the orchestration of a master aggregating unit.…”

Section: A Related Workmentioning

confidence: 99%

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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 Compression Approach: Rakness-based Vs Benchmark Solutionssupporting

confidence: 80%

Section: A Related Workmentioning

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 deck was instrumented with a network of 18 conventional wired accelerometers recording vertical acceleration time-histories at 200 Hz sampling rate for ∼10 min per hour from 12th July 2013 to 26th July 2014. The layout of the sensor network deployment is shown in Figure 8; more details about the bridge and the monitoring campaign can be found in Klis et al (2016).…”

Section: Assessment For Mode Shape Extraction Under Operational Loadimentioning

confidence: 99%

Output-Only Vibration-Based Monitoring of Civil Infrastructure via Sub-Nyquist/Compressive Measurements Supporting Reduced Wireless Data Transmission

Gkoktsi

Giaralis

Klis

et al. 2019

Front. Built Environ.

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The consideration of wireless acceleration sensors is highly promising for cost-effective output-only system identification in the context of operational modal analysis (OMA) of large-scale civil structures as they alleviate the need for wiring. However, practical monitoring implementations for OMA using wireless units suffer a number of drawbacks related to wireless transmission of densely sampled acceleration time-series including the energy self-sustainability of the sensing nodes. In this work, two recently proposed approaches for output-only modal identification addressing the above issues through balancing monitoring accuracy with data transmission costs are comparatively studied and numerically assessed using field recorded acceleration datasets from two different structures: (i) an operating onshore wind turbine, (ii) an open to traffic highway bridge. One approach utilizes non-uniform-in-time deterministic multi-coset sampling at sub-Nyquist rates to capture structural response acceleration time-series under ambient excitation assuming stationary signal conditions. In this approach, a power spectrum blind sampling technique is used to estimate the response acceleration power spectral density matrix from the low-rate sampled measurements and is coupled with the Frequency Domain Decomposition method of OMA. The other is a spectro-temporal compressive sensing approach which recovers response acceleration signals through time-series reconstruction in the time domain from sub-Nyquist non-uniform-in-time randomly sampled measurements. Prior knowledge of signal structure in the spectral domain is exploited through smart on-sensor operations and sensor/server communication. The benefits and limitations of the considered approaches are discussed and demonstrated by processing the field recorded datasets for different levels of signal compression and by estimating battery lifetime gains at a single sensor achieved by reduced data transmission. It is concluded that the two approaches are readily applicable in OMA of large-scale structures and can be used complementarily depending on the requirements of any particular acceleration monitoring campaign: time-series extraction for further interrogation vs. solely modal properties estimation.

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“…This work embedded a linear congruential generator (LCG) into a computational core of the Narada to produce the pseudo-random number and realized the accurate reconstruction. In 2017, R Klis et al 7 presented a study of CS-based vibration monitoring via spectro-temporal for wireless sensor networks, which provided a step-bystep time-series recovery process and a cost analysis in comparison of wireless and tethered sensing solutions.…”

Section: Introductionmentioning

confidence: 99%

Compressive sensing-based vibration signal reconstruction using sparsity adaptive subspace pursuit

Zhou

Liu

et al. 2018

Advances in Mechanical Engineering

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Wireless sensors produce large amounts of data in long-term online monitoring following the Shannon-Nyquist theorem, leading to a heavy burden on wireless communications and data storage. To address this problem, compressive sensing which allows wireless sensors to sample at a much lower rate than the Nyquist frequency has been considered. However, the lower rate sacrifices the integrity of the signal. Therefore, reconstruction from low-dimension measurement samples is necessary. Generally, the reconstruction needs the information of signal sparsity in advance, whereas it is usually unknown in practical applications. To address this issue, a sparsity adaptive subspace pursuit compressive sensing algorithm is deployed in this article. In order to balance the computational speed and estimation accuracy, a half-fold sparsity estimation method is proposed. To verify the effectiveness of this algorithm, several simulation tests were performed. First, the feasibility of subspace pursuit algorithm is verified using random sparse signals with five different sparsities. Second, the synthesized vibration signals for four different compression rates are reconstructed. The corresponding reconstruction correlation coefficient and root mean square error are demonstrated. The high correlation and low error result mean that the proposed algorithm can be applied in the vibration signal process. Third, implementation of the proposed approach for a practical vibration signal from an offshore structure is carried out. To reduce the effect of signal noise, the wavelet de-noising technique is used. Considering the randomness of the sampling, many reconstruction tests were carried out. Finally, to validate the reliability of the reconstructed signal, the structure modal parameters are calculated by the Eigensystem realization algorithm, and the result is only slightly different between original and reconstructed signal, which means that the proposed method can successfully save the modal information of vibration signals.

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Vibration monitoring via spectro-temporal compressive sensing for wireless sensor networks

Cited by 7 publications

References 1 publication

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

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

Output-Only Vibration-Based Monitoring of Civil Infrastructure via Sub-Nyquist/Compressive Measurements Supporting Reduced Wireless Data Transmission

Compressive sensing-based vibration signal reconstruction using sparsity adaptive subspace pursuit

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