Optimization of Signal Pre-Processing for the Integration of Cost-Effective Local Intelligence in Wireless Self-Powered Structural Health Monitoring

2008 ◽  
Vol 56 ◽  
pp. 459-468 ◽  
Author(s):  
Thomas Monnier ◽  
Philippe Guy ◽  
Mickaël Lallart ◽  
Lionel Petit ◽  
Daniel Guyomar ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Early Stage ◽  
Computational Cost ◽  
Cost Savings ◽  
Cost Effective ◽  
Extraction Process ◽  
Power Source ◽  
Positive Energy ◽  
Structural Health

Recent research in Structural Health Monitoring (SHM) showed the ability of guidedwave based sensors networks to detect, localize and classify damage in its early stage. But, most of them still require the wiring of numerous devices. To avoid this technical restraint, particularly in airborne structures, wireless SHM system offer mass and cost savings, but powering the devices remains heavy. In this paper, actuators and sensors are powered by piezoelectric microgenerators, which harvest energy from the environing mechanical stress. The efficiency of the extraction process is optimized by a non-linear processing of the piezovoltage named Synchronized Switch Harvesting. Previous work showed that such techniques provide a stand-alone power source, whose performances meet the requirements of Wireless Transmitters and Receivers. Indeed, each sensing node has to feature its own power source in order to acquire its logical autonomy and thus, provide decentralized intelligence to SHM network. Although the diagnosis will be centralized, the amount of data passed to the central core of the network should be reduced to preserve a positive energy balance of the node. Various algorithms are compared in terms of sensitivity and computational cost, the latter directly impacting the consumption.

scholarly journals Large-Area Resistive Strain Sensing Sheet for Structural Health Monitoring

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1386 ◽  
Author(s):  
Levent E. Aygun ◽  
Vivek Kumar ◽  
Campbell Weaver ◽  
Matthew Gerber ◽  
Sigurd Wagner ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Printed Circuit Board ◽  
Early Stage ◽  
Field Tests ◽  
Circuit Board ◽  
Gauge Factor ◽  
Peak Strain ◽  
Large Area ◽  
Structural Health

Damage significantly influences response of a strain sensor only if it occurs in the proximity of the sensor. Thus, two-dimensional (2D) sensing sheets covering large areas offer reliable early-stage damage detection for structural health monitoring (SHM) applications. This paper presents a scalable sensing sheet design consisting of a dense array of thin-film resistive strain sensors. The sensing sheet is fabricated using flexible printed circuit board (Flex-PCB) manufacturing process which enables low-cost and high-volume sensors that can cover large areas. The lab tests on an aluminum beam showed the sheet has a gauge factor of 2.1 and has a low drift of 1.5 μ ϵ / d a y . The field test on a pedestrian bridge showed the sheet is sensitive enough to track strain induced by the bridge’s temperature variations. The strain measured by the sheet had a root-mean-square (RMS) error of 7 μ ϵ r m s compared to a reference strain on the surface, extrapolated from fiber-optic sensors embedded within the bridge structure. The field tests on an existing crack showed that the sensing sheet can track the early-stage damage growth, where it sensed 600 μ ϵ peak strain, whereas the nearby sensors on a damage-free surface did not observe significant strain change.

scholarly journals Real-time structural health monitoring for concrete beams: a cost-effective ‘Industry 4.0’ solution using piezo sensors

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Arka Ghosh ◽  
David John Edwards ◽  
M. Reza Hosseini ◽  
Riyadh Al-Ameri ◽  
Jemal Abawajy ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Real Time ◽  
Health Monitoring ◽  
Industry 4.0 ◽  
Concrete Beams ◽  
Low Cost ◽  
Cost Effective ◽  
Content Type ◽  
Structural Health ◽  
Non Destructive

PurposeThis research paper adopts the fundamental tenets of advanced technologies in industry 4.0 to monitor the structural health of concrete beam members using cost-effective non-destructive technologies. In so doing, the work illustrates how a coalescence of low-cost digital technologies can seamlessly integrate to solve practical construction problems.Design/methodology/approachA mixed philosophies epistemological design is adopted to implement the empirical quantitative analysis of “real-time” data collected via sensor-based technologies streamed through a Raspberry Pi and uploaded onto a cloud-based system. Data was analysed using a hybrid approach that combined both vibration-characteristic-based method and linear variable differential transducers (LVDT).FindingsThe research utilises a novel digital research approach for accurately detecting and recording the localisation of structural cracks in concrete beams. This non-destructive low-cost approach was shown to perform with a high degree of accuracy and precision, as verified by the LVDT measurements. This research is testament to the fact that as technological advancements progress at an exponential rate, the cost of implementation continues to reduce to produce higher-accuracy “mass-market” solutions for industry practitioners.Originality/valueAccurate structural health monitoring of concrete structures necessitates expensive equipment, complex signal processing and skilled operator. The concrete industry is in dire need of a simple but reliable technique that can reduce the testing time, cost and complexity of maintenance of structures. This was the first experiment of its kind that seeks to develop an unconventional approach to solve the maintenance problem associated with concrete structures. This study merges industry 4.0 digital technologies with a novel low-cost and automated hybrid analysis for real-time structural health monitoring of concrete beams by fusing several multidisciplinary approaches into one integral technological configuration.

scholarly journals Vibration-Based Structural Health Monitoring Using Piezoelectric Transducers and Parametric t-SNE

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1716
Author(s):  
David Agis ◽  
Francesc Pozo
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Changes ◽  
Computational Cost ◽  
Principal Component ◽  
Piezoelectric Transducers ◽  
Majority Voting ◽  
Structural Health

In this paper, we evaluate the performance of the so-called parametric t-distributed stochastic neighbor embedding (P-t-SNE), comparing it to the performance of the t-SNE, the non-parametric version. The methodology used in this study is introduced for the detection and classification of structural changes in the field of structural health monitoring. This method is based on the combination of principal component analysis (PCA) and P-t-SNE, and it is applied to an experimental case study of an aluminum plate with four piezoelectric transducers. The basic steps of the detection and classification process are: (i) the raw data are scaled using mean-centered group scaling and then PCA is applied to reduce its dimensionality; (ii) P-t-SNE is applied to represent the scaled and reduced data as 2-dimensional points, defining a cluster for each structural state; and (iii) the current structure to be diagnosed is associated with a cluster employing two strategies: (a) majority voting; and (b) the sum of the inverse distances. The results in the frequency domain manifest the strong performance of P-t-SNE, which is comparable to the performance of t-SNE but outperforms t-SNE in terms of computational cost and runtime. When the method is based on P-t-SNE, the overall accuracy fluctuates between 99.5% and 99.75%.

Vibration-Based Structural Health Monitoring of a Simulated Beam with a Breathing Crack

2013 ◽  
Vol 569-570 ◽  
pp. 1093-1100 ◽  
Author(s):  
Jyrki Kullaa ◽  
Kari Santaoja ◽  
Anthony Eymery
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Crack Length ◽  
Health Monitoring ◽  
Early Stage ◽  
Novelty Detection ◽  
Principal Component ◽  
Extreme Value Statistics ◽  
Ratio Test ◽  
Structural Health

Cracking is a common type of failure in machines and structures. Cracks must be detected at an early stage before catastrophic failure. In structural health monitoring, changes in the vibration characteristics of the structure can be utilized in damage detection. A fatigue crack with alternating contact and non-contact phases results in a non-linear behaviour. This type of damage was simulated with a finite element model of a simply supported beam. The structure was monitored with a sensor array measuring transverse accelerations under random excitation. The objective was to determine the smallest crack length that can be detected. The effect of the sensor locations was also studied. Damage detection was performed using the generalized likelihood ratio test (GLRT) in time domain followed by principal component analysis (PCA). Extreme value statistics (EVS) were used for novelty detection. It was found that a crack in the bottom of the midspan could be detected once the crack length exceeded 10% of the beam height. The crack was correctly localized using the monitoring data.

scholarly journals Damage Localization in a Building Structure during Seismic Excitation

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Jesús Morales-Valdez ◽  
Luis Alvarez-Icaza ◽  
José A. Escobar
Keyword(s):  
Structural Health Monitoring ◽  
Real Time ◽  
Health Monitoring ◽  
Damage Assessment ◽  
Early Stage ◽  
Detection Methods ◽  
Identification System ◽  
Building Structures ◽  
Simple Method ◽  
Structural Health

Aging of buildings during their service life has attracted the attention of researchers on structural health monitoring (SHM). This paper is related with detecting damage in building structures at the earliest possible stage during seismic activity to facilitate decision-making on evacuation before physical inspection is possible. For this, a simple method for damage assessment is introduced to identify the damage story of multistory buildings from acceleration measurements under a wave propagation approach. In this work, damage is assumed as reduction in shear wave velocities and changes in damping ratios that are directly related with stiffness loss. Most damage detection methods are off-line processes; this is not the case with this method. First, a real-time identification system is introduced to estimate the current parameters to be compared with nominal values to detect any changes in the characteristics that may indicate damage in the building. In addition, this identification system is robust to constant disturbances and measurement noise. The time needed to complete parameter identification is shorter compared to the typically wave method, and the damage assessment can keep up with the data flow in real time. Finally, using a robust threshold, postprocess of the compared signal is performed to find the location of the possible damage. The performance of the proposed method is demonstrated through experiments on a reduced-scale five-story building, showing the ability of the proposed method to improve early stage structural health monitoring.

scholarly journals Design and Development of Magnetostrictive Actuators and Sensors for Structural Health Monitoring

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 711 ◽  
Author(s):  
Jamin Daniel Selvakumar Vincent ◽  
Michelle Rodrigues ◽  
Zhaoyuan Leong ◽  
Nicola A. Morley
Keyword(s):  
Structural Health Monitoring ◽  
Surface Area ◽  
Health Monitoring ◽  
Cost Effective ◽  
Structural Health ◽  
Rlc Circuit ◽  
Magnetostrictive Actuators ◽  
Carbon Fibre Reinforced Polymer ◽  
Fibre Reinforced Polymer ◽  
New Sensors

Carbon Fibre Reinforced Polymer composite (CFRP) is widely used in the aerospace industry, but is prone to delamination, which is a major causes of failure. Structural Health Monitoring (SHM) systems need to be developed to determine the damage occurring within it. Our motivation is to design cost-effective new sensors and a data acquisition system for magnetostrictive structural health monitoring of aerospace composites using a simple RLC circuit. The developed system is tested on magnetostrictive FeSiB and CoSiB actuator ribbons using a bending rig. Our results show detectable sensitivity of inductors as low as 0.6 μH for a bending rig radii between 600 to 300 mm (equivalent to 0.8 to 1.7 mStrain), which show a strain sensitivity resolution of 0.01 μStrain (surface area: ~36 mm2). This value is at the detectability limit of our fabricated system. The best resolution (1.86 μStrain) was obtained from a 70-turn copper (~64 μH) wire inductor (surface area: ~400 mm2) that was paired with a FeSiB actuator.

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