scholarly journals A Method of Data Recovery Based on Compressive Sensing in Wireless Structural Health Monitoring

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Sai Ji ◽  
Yajie Sun ◽  
Jian Shen
Keyword(s):  
Structural Health Monitoring ◽  
Compressive Sensing ◽  
Health Monitoring ◽  
Base Station ◽  
Data Recovery ◽  
Aluminum Plate ◽  
Data Loss ◽  
Response Signal ◽  
Structural Health ◽  
Data Vector

In practical structural health monitoring (SHM) process based on wireless sensor network (WSN), data loss often occurs during the data transmission between sensor nodes and the base station, which will affect the structural data analysis and subsequent decision making. In this paper, a method of recovering lost data in WSN based on compressive sensing (CS) is proposed. Compared with the existing methods, it is a simple and stable data recovery method and can obtain lower recovery data error for one-dimensional SHM’s data loss. First, response signalxis measured onto the measurement data vectorythrough inner products with random vectors. Note thatyis the linear projection ofxandyis permitted to be lost in part during the transmission. Next, when the base station receives the incomplete data, the response signalxcan be reconstructed from the data vectoryusing the CS method. Finally, the test of active structural damage identification on LF-21M aviation antirust aluminum plate is proposed. The response signal gathered from the aluminum plate is used to verify the data recovery ability of the proposed method.

Compressed sensing–based electromechanical admittance data loss recovery for concrete structural health monitoring

2020 ◽  
pp. 147592172095064
Author(s):  
Hedong Li ◽  
Demi Ai ◽  
Hongping Zhu ◽  
Hui Luo
Keyword(s):  
Structural Health Monitoring ◽  
Convex Optimization ◽  
Compressed Sensing ◽  
Health Monitoring ◽  
Matching Pursuit ◽  
Orthogonal Matching Pursuit ◽  
Data Recovery ◽  
Sampled Data ◽  
Data Loss ◽  
Structural Health

Considerable amount of electromechanical admittance data needs to be collected, transmitted and stored during in-situ and long-term structural health monitoring applications, and data loss could be inevitably met when processing the monitoring electromechanical admittance signals. In this article, an innovative compressed sensing–based approach is proposed to implement data recovery for electromechanical admittance technique–based concrete structural health monitoring. The basis of this approach is to first project the original conductance signature onto an observation vector as sampled data, and then transmit the observation vector with data loss to storage station, and finally recover the missing data via a compressed sensing process. For comparison, both convex optimization theory and orthogonal matching pursuit algorithm are introduced to accomplish the compressed sensing–based electromechanical admittance data loss recovery. Prior detection test of a concrete cube subjected to varied temperatures and practical monitoring experiment of full-scale concrete shield tunnel segment undergone bolt-loosened defects are utilized to validate the feasibility of the proposed approach. In lost electromechanical admittance data recovery process, two types of data loss, namely, single-consecutive-segment loss and multiple-consecutive-segment losses, in sampled data are taken into consideration for sufficiently interpreting the effectiveness and accuracy of the convex optimization and orthogonal matching pursuit approaches. In the temperature recognition and damage identification stage, amplitude and frequency shifts in resonance peaks, cooperated with a common statistical index called root-mean-squared-deviation, are harnessed to achieve the goal after the lossy conductance signatures are recovered. The results show that the orthogonal matching pursuit–based data recovery approach is superior to the convex optimization approach because of its smaller calculation of consumption as well as lower recovered errors.

Group sparsity-aware convolutional neural network for continuous missing data recovery of structural health monitoring

2020 ◽  
pp. 147592172093174
Author(s):  
Zhiyi Tang ◽  
Yuequan Bao ◽  
Hui Li
Keyword(s):  
Neural Network ◽  
Missing Data ◽  
Structural Health Monitoring ◽  
Convolutional Neural Network ◽  
Health Monitoring ◽  
Data Recovery ◽  
Data Matrix ◽  
Data Loss ◽  
Group Sparsity ◽  
Structural Health

In structural health monitoring, data quality is crucial to the performance of data-driven methods for structural damage identification, condition assessment, and safety warning. However, structural health monitoring systems often suffer from data imperfection, resulting in some entries being unusable in a data matrix. Discrete missing points are relatively easy to recover based on known adjacent points, whereas segments of continuous missing data are more common and also more challenging to recover in a practical scenario. Formulating the data recovery task as an optimization problem for matrix completion, we present a convolutional neural network to achieve simultaneous recovery for multi-channel data with the awareness of group sparsity. The data recovery process based on compressive sensing is formulated as a regression problem and achieved in the neural network. The basis matrix is utilized as the input and the incomplete data matrix as the output to provide partial information for approximation. Basis coefficient optimization is performed via convolutional operation. Group sparsity regularization is applied while updating the kernel of the convolutional layer. The recovery can be readily obtained after optimization (training) without further validation and testing. The proposed method does not need intact data prepared in advance for training; also, it can handle sporadic data loss and make the most of interrupted information. Recovery ability evaluations on synthetic data, field-test data, and monitoring data of seismic response indicate that the proposed method achieves a good recovery result with high loss ratio and continuous data loss. The code is available at https://github.com/dawnnao/Group-sparsity-aware-CNN .

scholarly journals Temperature Effects on Electromechanical Response of Deposited Piezoelectric Sensors Used in Structural Health Monitoring of Aerospace Structures

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2805 ◽  
Author(s):  
Hamidreza Hoshyarmanesh ◽  
Mojtaba Ghodsi ◽  
Minjae Kim ◽  
Hyung Hee Cho ◽  
Hyung-Ho Park
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Mechanical Impedance ◽  
Peak Shift ◽  
Aluminum Plate ◽  
Distinct Advantage ◽  
Compressor Blades ◽  
Turbine Engine ◽  
Electromechanical Impedance ◽  
Structural Health

Turbomachine components used in aerospace and power plant applications preferably require continuous structural health monitoring at various temperatures. The structural health of pristine and damaged superalloy compressor blades of a gas turbine engine was monitored using real electro-mechanical impedance of deposited thick film piezoelectric transducers at 20 and 200 °C. IVIUM impedance analyzer was implemented in laboratory conditions for damage detection in superalloy blades, while a custom-architected frequency-domain transceiver circuit was used for semi-field circumstances. Recorded electromechanical impedance signals at 20 and 200 °C acquired from two piezoelectric wafer active sensors bonded to an aluminum plate, near and far from the damage, were initially utilized for accuracy and reliability verification of the transceiver at temperatures >20 °C. Damage formation in both the aluminum plate and blades showed a peak shift in the swept frequency along with an increase in the amplitude and number of impedance peaks. The thermal energy at 200 °C, on the other hand, enforces a further subsequent peak shift in the impedance signal to pristine and damaged parts such that the anti-resonance frequency keeps reducing as the temperature increases. The results obtained from the impedance signals of both piezoelectric wafers and piezo-films, revealed that increasing the temperature somewhat decreased the real impedance amplitude and the number of anti-resonance peaks, which is due to an increase in permittivity and capacitance of piezo-sensors. A trend is also presented for artificial intelligence training purposes to distinguish the effect of the temperature versus damage formation in sample turbine compressor blades. Implementation of such a monitoring system provides a distinct advantage to enhance the safety and functionality of critical aerospace components working at high temperatures subjected to crack, wear, hot-corrosion and erosion.

Sign in / Sign up

Export Citation Format

Share Document