scholarly journals Damage Detection and Localization from Dense Network of Strain Sensors

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Simon Laflamme ◽  
Liang Cao ◽  
Eleni Chatzi ◽  
Filippo Ubertini
Keyword(s):  
Damage Detection ◽  
Damage Identification ◽  
Low Cost ◽  
Strain Sensors ◽  
Dense Network ◽  
Virtual Sensors ◽  
Large Structures ◽  
Complex Engineering ◽  
Detection And Localization

Structural health monitoring of large systems is a complex engineering task due to important practical issues. When dealing with large structures, damage diagnosis, localization, and prognosis necessitate a large number of sensors, which is a nontrivial task due to the lack of scalability of traditional sensing technologies. In order to address this challenge, the authors have recently proposed a novel sensing solution consisting of a low-cost soft elastomeric capacitor that transduces surface strains into measurable changes in capacitance. This paper demonstrates the potential of this technology for damage detection, localization, and prognosis when utilized in dense network configurations over large surfaces. A wind turbine blade is adopted as a case study, and numerical simulations demonstrate the effectiveness of a data-driven algorithm relying on distributed strain data in evidencing the presence and location of damage, and sequentially ranking its severity. Numerical results further show that the soft elastomeric capacitor may outperform traditional strain sensors in damage identification as it provides additive strain measurements without any preferential direction. Finally, simulation with reconstruction of measurements from missing or malfunctioning sensors using the concepts of virtual sensors and Kriging demonstrates the robustness of the proposed condition assessment methodology for sparser or malfunctioning grids.

scholarly journals Eigenfrequency-Based Bayesian Approach for Damage Identification in Catenary Poles

2021 ◽  
Vol 6 (4) ◽  
pp. 57
Author(s):  
Feras Alkam ◽  
Tom Lahmer
Keyword(s):  
Damage Detection ◽  
High Speed ◽  
Damage Identification ◽  
Low Cost ◽  
High Speed Train ◽  
Noise Levels ◽  
Acceptable Error ◽  
Stochastic Framework ◽  
Identification Approach

This study proposes an efficient Bayesian, frequency-based damage identification approach to identify damages in cantilever structures with an acceptable error rate, even at high noise levels. The catenary poles of electric high-speed train systems were selected as a realistic case study to cover the objectives of this study. Compared to other frequency-based damage detection approaches described in the literature, the proposed approach is efficiently able to detect damages in cantilever structures to higher levels of damage detection, namely identifying both the damage location and severity using a low-cost structural health monitoring (SHM) system with a limited number of sensors; for example, accelerometers. The integration of Bayesian inference, as a stochastic framework, in the proposed approach, makes it possible to utilize the benefit of data fusion in merging the informative data from multiple damage features, which increases the quality and accuracy of the results. The findings provide the decision-maker with the information required to manage the maintenance, repair, or replacement procedures.

Circumference Damage Identification in a Pipe Using Mode Conversion of Longitudinal Guided Wave

2013 ◽  
Vol 395-396 ◽  
pp. 787-791
Author(s):  
Jing Wu ◽  
Wei Wei Zhang
Keyword(s):  
Damage Detection ◽  
Damage Identification ◽  
Mode Conversion ◽  
Guided Wave ◽  
Symmetric Mode ◽  
Axially Symmetric ◽  
Mode Transformation ◽  
Damage Location

This paper aims to develop a method to identify the damage location in circumference direction of a pipe using mode transformation of longitudinal guided wave. The corrosion-like damage in bimetal pipe is considered. Case study that damage detection for a bimetal pipe is used to show the validity and advantage of the proposed method. It can be found that the axially symmetric mode guided wave encounter the damage and the three modes were received in reflection. The damage location in circumferential directions could be identified by conversed modes measured at one position. The simulation shows a good performance.

scholarly journals Measurement of Acceleration Response Functions with Scalable Low-Cost Devices. An Application to the Experimental Modal Analysis

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6637
Author(s):  
Alvaro Magdaleno ◽  
Juan Villacorta ◽  
Lara del-Val ◽  
Alberto Izquierdo ◽  
Antolin Lorenzana
Keyword(s):  
Modal Analysis ◽  
Lower Cost ◽  
Low Cost ◽  
Micro Electro Mechanical System ◽  
Response Functions ◽  
Large Structures ◽  
Frequency Domains ◽  
Recorded Data ◽  
Applied Forces

One of the most popular options in the Structural Health Monitoring field is the tracking of the modal parameters, which are estimated through the frequency response functions of the structure, usually in the form of accelerances, which are computed as the ratio between the measured accelerations and the applied forces. This requires the use of devices capable of synchronously recording accelerations at several points of the structure at high sampling rates and the subsequent computational analysis using the recorded data. To this end, this work presents and validates a new scalable acquisition system based on multiple myRIO devices and digital MEMS (Micro-Electro-Mechanical System) accelerometers, intended for modal analysis of large structures. A simple form of this system was presented by the authors in a previous work, showing that a single board with some accelerometers connected to it got to obtain high quality measurements in both time and frequency domains. Now, a larger system composed by several slave boards connected and synchronized to a master one is presented. Delays lower than 100 ns are found between the synchronised channels of the proposed system. For validation purposes, a case study is presented where the devices are deployed on a timber platform to estimate its modal properties, which are compared with the ones provided by a commercial system, based on analog accelerometers, to show that similar results are obtained at a significantly lower cost.

Towards UAV-Based Post-Disaster Damage Detection and Localization: Hurricane Sally Case Study

2022 ◽  
Author(s):  
Andrew Clevenger ◽  
Rafael de Sa Lowande ◽  
Hakki Erhan Sevil ◽  
Arash Mahyari
Keyword(s):  
Damage Detection ◽  
Detection And Localization ◽  
Post Disaster

scholarly journals Damage Detection and Localization under Variable Environmental Conditions Using Compressed and Reconstructed Bayesian Virtual Sensor Data

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 306
Author(s):  
Jyrki Kullaa
Keyword(s):  
Damage Detection ◽  
Data Compression ◽  
Sensor Network ◽  
Measurement Data ◽  
Sensor Data ◽  
Extreme Value Statistics ◽  
Virtual Sensors ◽  
Actual Measurement ◽  
Redundant Data ◽  
Detection And Localization

Structural health monitoring (SHM) with a dense sensor network and repeated vibration measurements produces lots of data that have to be stored. If the sensor network is redundant, data compression is possible by storing the signals of selected Bayesian virtual sensors only, from which the omitted signals can be reconstructed with higher accuracy than the actual measurement. The selection of the virtual sensors for storage is done individually for each measurement based on the reconstruction accuracy. Data compression and reconstruction for SHM is the main novelty of this paper. The stored and reconstructed signals are used for damage detection and localization in the time domain using spatial or spatiotemporal correlation. Whitening transformation is applied to the training data to take the environmental or operational influences into account. The first principal component of the residuals is used to localize damage and also to design the extreme value statistics control chart for damage detection. The proposed method was studied with a numerical model of a frame structure with a dense accelerometer or strain sensor network. Only five acceleration or three strain signals out of the total 59 signals were stored. The stored and reconstructed data outperformed the raw measurement data in damage detection and localization.

scholarly journals Spiking Neural Network-Based Near-Sensor Computing for Damage Detection in Structural Health Monitoring

2021 ◽  
Vol 13 (8) ◽  
pp. 219
Author(s):  
Francesco Barchi ◽  
Luca Zanatta ◽  
Emanuele Parisi ◽  
Alessio Burrello ◽  
Davide Brunelli ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Damage Detection ◽  
Execution Time ◽  
Sensor Node ◽  
Data Transfer ◽  
Low Cost ◽  
Access Time ◽  
Virtual Sensors ◽  
The Impact ◽  
Spi Interface

In this work, we present an innovative approach for damage detection of infrastructures on-edge devices, exploiting a brain-inspired algorithm. The proposed solution exploits recurrent spiking neural networks (LSNNs), which are emerging for their theoretical energy efficiency and compactness, to recognise damage conditions by processing data from low-cost accelerometers (MEMS) directly on the sensor node. We focus on designing an efficient coding of MEMS data to optimise SNN execution on a low-power microcontroller. We characterised and profiled LSNN performance and energy consumption on a hardware prototype sensor node equipped with an STM32 embedded microcontroller and a digital MEMS accelerometer. We used a hardware-in-the-loop environment with virtual sensors generating data on an SPI interface connected to the physical microcontroller to evaluate the system with a data stream from a real viaduct. We exploited this environment also to study the impact of different on-sensor encoding techniques, mimicking a bio-inspired sensor able to generate events instead of accelerations. Obtained results show that the proposed optimised embedded LSNN (eLSNN), when using a spike-based input encoding technique, achieves 54% lower execution time with respect to a naive LSNN algorithm implementation present in the state-of-the-art. The optimised eLSNN requires around 47 kCycles, which is comparable with the data transfer cost from the SPI interface. However, the spike-based encoding technique requires considerably larger input vectors to get the same classification accuracy, resulting in a longer pre-processing and sensor access time. Overall the event-based encoding techniques leads to a longer execution time (1.49×) but similar energy consumption. Moving this coding on the sensor can remove this limitation leading to an overall more energy-efficient monitoring system.

scholarly journals Brief communication: monitoring a soft-rock coastal cliff using webcams and strain sensors

2021 ◽  
Author(s):  
Diego Guenzi ◽  
Danilo Godone ◽  
Paolo Allasia ◽  
Nunzio Luciano Fazio ◽  
Michele Perrotti ◽  
...  
Keyword(s):  
Open Source ◽  
Low Cost ◽  
Soft Rock ◽  
Strain Sensors ◽  
Apulia Region ◽  
Coastal Cliff ◽  
Open Source Hardware ◽  
Potential Precursor

Abstract. In this brief communication, we describe a case study about monitoring a soft-rock coastal cliff using webcams and strain sensors, located in the Apulia region (southeastern Italy). In this urban and touristic area, coastal recession is extremely rapid and rockfalls are very frequent. Using low-cost and open source hardware and software, we are monitoring the area, trying to correlate both meteorological information with measures obtained from crack-meters and webcams, aiming to recognize potential precursor signals that may trigger instability phenomena.

Application of Vision Based Damage Detection for Real Civil Engineering Structure

2013 ◽  
Vol 588 ◽  
pp. 22-32 ◽  
Author(s):  
Piotr Kohut ◽  
Krzysztof Holak ◽  
Tadeusz Uhl ◽  
Jędrzej Mączak ◽  
Przemysław Szulim
Keyword(s):  
Global Change ◽  
Damage Detection ◽  
Health Monitoring ◽  
Data Transmission ◽  
Dynamic Properties ◽  
Detection Methods ◽  
Static Deflection ◽  
Deflection Curve ◽  
Large Structures ◽  
Detection And Localization

Structural Health Monitoring (SHM) is an emerging field of technology that involves the integration of sensors, data transmission, processing and analysis for detection, as well as localization and assessment of damage which can lead to its failure in the future [1,. In general, SHM methods can be divided into two groups: local and global ones. The second group can be applied if a global change in the geometry of a structure can be observed. In practice, the most commonly used methods of damage detection are based on the analysis of variations in various dynamic properties caused by damage [3,. However, the excitation of large structures can be costly and difficult. The acquisition of static deflection requires much less effort, which makes the damage detection methods based on changes in deflection curves more attractive for practical use [5-1. Damage detection and localization methods require a densely sampled deflection curve.

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