Signal Reconstruction from Mobile Sensors Network Using Matrix Completion Approach

Eshkevari, Soheil Sadeghi; Pakzad, Shamim N.

doi:10.1007/978-3-030-12684-1_7

Cited by 12 publications

(9 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At this time, STRIDEX by is the one of the procedure that is capable of performing a complete identification using mobile sensor data. Sadeghi Eshkevari et al [2020] has recently proposed an alternative approach as well with a different sensing setting.…”

Section: Toward Infrastructure Vibration Crowdsourcingmentioning

confidence: 99%

“…Further mathematical details for MSR function can be found in Matarazzo and Pakzad [2016b]. In addition, a more advanced signal reconstruction method is proposed by Eshkevari and Pakzad [2020] which enables data estimation on probing locations under high irregularities in the mobile sensors network.…”

Section: Review Of System Identification Using Mobile Sensor Networkmentioning

confidence: 99%

See 1 more Smart Citation

Bridge Modal Identification using Acceleration Measurements within Moving Vehicles

Eshkevari,

Matarazzo,

Pakzad

2020

Preprint

Self Cite

View full text Add to dashboard Cite

Vehicles commuting over bridge structures respond dynamically to the bridge's vibrations. An acceleration signal collected within a moving vehicle contains a trace of the bridge's structural response, but also includes other sources such as the vehicle suspension system and surface roughness-induced vibrations. This paper introduces two general methods for the bridge system identification using data exclusively collected by a network of moving vehicles. The contributions of the vehicle suspension system are removed by deconvolving the vehicle response in frequency domain. The first approach utilizes the vehicle transfer function, and the second uses ensemble empirical modal decomposition (EEMD). EEMD is a geometric based blind source separation (BSS) tool that is able to extract sources out of a statically or convolutionally mixed signal. Next, roughness-induced vibrations are extracted through a novel application of second-order blind identification (SOBI) method. After these two processes the resulting signal is equivalent to the readings of mobile sensors that scan the bridge's dynamic response. Structural modal identification using mobile sensor data has been recently made possible with the extended structural modal identification using expectation maximization (STRIDEX) algorithm. The processed mobile sensor data is analyzed using STRIDEX to identify the modal properties of the bridge. The performance of the methods are validated on numerical case studies of a long single-span bridge with a network of moving vehicles collecting data while in motion. The analyses consider three road surface roughness patterns. Results show that the proposed algorithms are successful in extracting pure bridge vibrations, and produce accurate and comprehensive modal properties of the bridge. The study shows that the proposed transfer function method can efficiently deconvolve the linear dynamics of a moving vehicle. EEMD method is able to extract vehicle dynamic response without a-priori information about the vehicle. In addition, proposed identification methods provide secondary information about the roughness pattern and the vehicle. This study is the first proposed methodology for complete bridge modal identification, including operational natural frequencies, mode shapes and damping ratios using moving vehicles sensor data.

show abstract

Section: Toward Infrastructure Vibration Crowdsourcingmentioning

confidence: 99%

Section: Review Of System Identification Using Mobile Sensor Networkmentioning

confidence: 99%

Bridge Modal Identification using Acceleration Measurements within Moving Vehicles

Eshkevari,

Matarazzo,

Pakzad

2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…To this end, there are several studies, including Grossmann et al (2009) and Matarazzo and Pakzad (2016b), which discuss that the model order decreases significantly with an increase in missing data. Additionally, Sadeghi Eshkevari and Pakzad (2019) emphasize that randomness in missing data results in lower model order selection.…”

Section: Regression Parameter Estimationmentioning

confidence: 99%

Parameter Estimation of Autoregressive-Exogenous and Autoregressive Models Subject to Missing Data Using Expectation Maximization

Horner

Pakzad

Gulgec

2019

Front. Built Environ.

Self Cite

View full text Add to dashboard Cite

Missing observations may present several problems for statistical analyses on datasets if they are not accounted for. This paper concerns a model-based missing data analysis procedure to estimate the parameters of regression models fit to datasets with missing observations. Both autoregressive-exogenous (ARX) and autoregressive (AR) models are considered. These models are both used to simulate datasets, and are fit to existing structural vibration data, after which observations are removed. A missing data analysis is performed using maximum-likelihood estimation, the expectation maximization (EM) algorithm, and the Kalman filter to fill in missing observations and regression parameters, and compare them to estimates for the complete datasets. Regression parameters from these fits to structural vibration data can thereby be used as damage-sensitive features. Favorable conditions for accurate parameter estimation are found to include lower percentages of missing data, parameters of similar magnitude with one another, and selected model orders similar to those true to the dataset. Favorable conditions for dataset reconstruction are found to include random and periodic missing data patterns, lower percentages of missing data, and proper model order selection. The algorithm is particularly robust to varied noise levels.

show abstract

“…Matarazzo et al [ 33 , 34 ] introduced the “structural identification using expectation maximization (STRIDE)” method for mode shape identification from mobile sensors. Eshkevari et al [ 35 , 36 , 37 ] formulated mobile sensing data as a sparse matrix with missing values. They employed alternating least-square (ALS) for matrix completion, followed by principal component analysis (PCA) and structured optimization analysis (SOA) for modal identification.…”

Section: Introductionmentioning

confidence: 99%

Full-Field Vibration Response Estimation from Sparse Multi-Agent Automatic Mobile Sensors Using Formation Control Algorithm

Jana,

Nagarajaiah

2023

Sensors

View full text Add to dashboard Cite

In structural vibration response sensing, mobile sensors offer outstanding benefits as they are not dedicated to a certain structure; they also possess the ability to acquire dense spatial information. Currently, most of the existing literature concerning mobile sensing involves human drivers manually driving through the bridges multiple times. While self-driving automated vehicles could serve for such studies, they might entail substantial costs when applied to structural health monitoring tasks. Therefore, in order to tackle this challenge, we introduce a formation control framework that facilitates automatic multi-agent mobile sensing. Notably, our findings demonstrate that the proposed formation control algorithm can effectively control the behavior of the multi-agent systems for structural response sensing purposes based on user choice. We leverage vibration data collected by these mobile sensors to estimate the full-field vibration response of the structure, utilizing a compressive sensing algorithm in the spatial domain. The task of estimating the full-field response can be represented as a spatiotemporal response matrix completion task, wherein the suite of multi-agent mobile sensors sparsely populates some of the matrix’s elements. Subsequently, we deploy the compressive sensing technique to obtain the dense full-field vibration complete response of the structure and estimate the reconstruction accuracy. Results obtained from two different formations on a simply supported bridge are presented in this paper, and the high level of accuracy in reconstruction underscores the efficacy of our proposed framework. This multi-agent mobile sensing approach showcases the significant potential for automated structural response measurement, directly applicable to health monitoring and resilience assessment objectives.

show abstract

Signal Reconstruction from Mobile Sensors Network Using Matrix Completion Approach

Cited by 12 publications

References 23 publications

Bridge Modal Identification using Acceleration Measurements within Moving Vehicles

Bridge Modal Identification using Acceleration Measurements within Moving Vehicles

Parameter Estimation of Autoregressive-Exogenous and Autoregressive Models Subject to Missing Data Using Expectation Maximization

Full-Field Vibration Response Estimation from Sparse Multi-Agent Automatic Mobile Sensors Using Formation Control Algorithm

Contact Info

Product

Resources

About