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 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.

Time Series Based Damage Detection and Localization in an Offshore Platform Using Wireless Sensor Networks

2014 ◽  
Author(s):  
Harsh Nandan ◽  
Eric Abrahamson ◽  
Xiangyu Wang ◽  
Carl Brinkmann
Keyword(s):  
Time Series ◽  
Wireless Sensor Network ◽  
Damage Detection ◽  
Sensor Network ◽  
Structural Integrity ◽  
Ar Model ◽  
Wireless Sensor ◽  
Analysis Method ◽  
Stiffness Reduction ◽  
Detection And Localization

Continuous structural integrity monitoring (SIM) can be a valuable complementary tool to the current practice of periodic inspections in detecting damage in jacket platforms. This paper demonstrates the technical feasibility of adopting the recent advances in onshore SIM technology for offshore jacket platforms. Both the analysis method and hardware technology are investigated. To demonstrate the feasibility of the analysis method, a time series based damage detection and localization algorithm is evaluated. Nodal acceleration and brace strain responses from a jacket platform computer model are simulated and used to determine the Autoregressive (AR) model coefficients. Mahalanobis distance calculated from the first 10 AR coefficients is used as the damage feature (DF). The DF’s from three different damage cases comprising of missing member, dented member (stiffness reduction), and cracked member (nonlinear behavior), respectively, are compared with those from the healthy baseline case to detect and localize damage. To demonstrate the feasibility of hardware technology, a survey of the state-of-the-art in wireless sensor network technology is conducted. The survey shows that wireless accelerometers and strain gauges packaged for underwater use can be fitted in a wireless sensor network throughout the jacket using the electromagnetic communication approach. A conceptual configuration of underwater damage detection wireless sensor network for offshore jacket platforms is presented.

scholarly journals Ultrasonic Flaw Imaging via Multipath Exploitation

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Yimin D. Zhang ◽  
Xizhong Shen ◽  
Ramazan Demirli ◽  
Moeness G. Amin
Keyword(s):  
Ultrasonic Imaging ◽  
Real Data ◽  
Sensor Data ◽  
Point Spread Functions ◽  
Virtual Sensors ◽  
Ultrasonic Flaw ◽  
Virtual Array ◽  
Bottom Side ◽  
Material Conditions ◽  
Detection And Localization

We consider ultrasonic imaging for the visualization of flaws in a material. Ultrasonic imaging is a powerful nondestructive testing (NDT) tool which assesses material conditions via the detection, localization, and classification of flaws inside a structure. We utilize reflections of ultrasonic signals which occur when encountering different media and interior boundaries. These reflections can be cast as direct paths to the target corresponding to the virtual sensors appearing on the top and bottom side of the target. Some of these virtual sensors constitute a virtual aperture, whereas in others, the aperture changes with the transmitter position. Exploitations of multipath extended virtual array apertures provide enhanced imaging capability beyond the limitation of traditional multisensor approaches. The waveforms observed at the physical as well as the virtual sensors yield additional measurements corresponding to different aspect angles, thus allowing proper multiview imaging of flaws. We derive the wideband point spread functions for dominant multipaths and show that fusion of physical and virtual sensor data improves the flaw perimeter detection and localization performance. The effectiveness of the proposed multipath exploitation approach is demonstrated using real data.

Sparse damage detection via the elastic net method using modal data

2021 ◽  
pp. 147592172110219
Author(s):  
Rongrong Hou ◽  
Xiaoyou Wang ◽  
Yong Xia
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Damage Identification ◽  
Measurement Data ◽  
Elastic Net ◽  
Sensitivity Matrix ◽  
Regularization Technique ◽  
Modal Data ◽  
Regularization Techniques ◽  
Net Method

The l1 regularization technique has been developed for damage detection by utilizing the sparsity feature of structural damage. However, the sensitivity matrix in the damage identification exhibits a strong correlation structure, which does not suffice the independency criteria of the l1 regularization technique. This study employs the elastic net method to solve the problem by combining the l1 and l2 regularization techniques. Moreover, the proposed method enables the grouped structural damage being identified simultaneously, whereas the l1 regularization cannot. A numerical cantilever beam and an experimental three-story frame are utilized to demonstrate the effectiveness of the proposed method. The results showed that the proposed method is able to accurately locate and quantify the single and multiple damages, even when the number of measurement data is much less than the number of elements. In particular, the present elastic net technique can detect the grouped damaged elements accurately, whilst the l1 regularization method cannot.

Using Machine Learning for Dependable Outlier Detection in Environmental Monitoring Systems

2021 ◽  
Vol 5 (3) ◽  
pp. 1-30
Author(s):  
Gonçalo Jesus ◽  
António Casimiro ◽  
Anabela Oliveira
Keyword(s):  
Machine Learning ◽  
Environmental Monitoring ◽  
Data Quality ◽  
Outlier Detection ◽  
Prediction Models ◽  
Sensor Data ◽  
Natural Phenomenon ◽  
Monitoring Systems ◽  
Data Errors ◽  
Redundant Data

Sensor platforms used in environmental monitoring applications are often subject to harsh environmental conditions while monitoring complex phenomena. Therefore, designing dependable monitoring systems is challenging given the external disturbances affecting sensor measurements. Even the apparently simple task of outlier detection in sensor data becomes a hard problem, amplified by the difficulty in distinguishing true data errors due to sensor faults from deviations due to natural phenomenon, which look like data errors. Existing solutions for runtime outlier detection typically assume that the physical processes can be accurately modeled, or that outliers consist in large deviations that are easily detected and filtered by appropriate thresholds. Other solutions assume that it is possible to deploy multiple sensors providing redundant data to support voting-based techniques. In this article, we propose a new methodology for dependable runtime detection of outliers in environmental monitoring systems, aiming to increase data quality by treating them. We propose the use of machine learning techniques to model each sensor behavior, exploiting the existence of correlated data provided by other related sensors. Using these models, along with knowledge of processed past measurements, it is possible to obtain accurate estimations of the observed environment parameters and build failure detectors that use these estimations. When a failure is detected, these estimations also allow one to correct the erroneous measurements and hence improve the overall data quality. Our methodology not only allows one to distinguish truly abnormal measurements from deviations due to complex natural phenomena, but also allows the quantification of each measurement quality, which is relevant from a dependability perspective. We apply the methodology to real datasets from a complex aquatic monitoring system, measuring temperature and salinity parameters, through which we illustrate the process for building the machine learning prediction models using a technique based on Artificial Neural Networks, denoted ANNODE ( ANN Outlier Detection ). From this application, we also observe the effectiveness of our ANNODE approach for accurate outlier detection in harsh environments. Then we validate these positive results by comparing ANNODE with state-of-the-art solutions for outlier detection. The results show that ANNODE improves existing solutions regarding accuracy of outlier detection.

scholarly journals Biosignal Compression Toolbox for Digital Biomarker Discovery

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 516
Author(s):  
Brinnae Bent ◽  
Baiying Lu ◽  
Juseong Kim ◽  
Jessilyn P. Dunn
Keyword(s):  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Data Compression ◽  
Biomarker Discovery ◽  
Singular Value ◽  
Sensor Data ◽  
Discrete Wavelet ◽  
Wearable Sensor ◽  
Huffman Encoding ◽  
Value Decomposition

A critical challenge to using longitudinal wearable sensor biosignal data for healthcare applications and digital biomarker development is the exacerbation of the healthcare “data deluge,” leading to new data storage and organization challenges and costs. Data aggregation, sampling rate minimization, and effective data compression are all methods for consolidating wearable sensor data to reduce data volumes. There has been limited research on appropriate, effective, and efficient data compression methods for biosignal data. Here, we examine the application of different data compression pipelines built using combinations of algorithmic- and encoding-based methods to biosignal data from wearable sensors and explore how these implementations affect data recoverability and storage footprint. Algorithmic methods tested include singular value decomposition, the discrete cosine transform, and the biorthogonal discrete wavelet transform. Encoding methods tested include run-length encoding and Huffman encoding. We apply these methods to common wearable sensor data, including electrocardiogram (ECG), photoplethysmography (PPG), accelerometry, electrodermal activity (EDA), and skin temperature measurements. Of the methods examined in this study and in line with the characteristics of the different data types, we recommend direct data compression with Huffman encoding for ECG, and PPG, singular value decomposition with Huffman encoding for EDA and accelerometry, and the biorthogonal discrete wavelet transform with Huffman encoding for skin temperature to maximize data recoverability after compression. We also report the best methods for maximizing the compression ratio. Finally, we develop and document open-source code and data for each compression method tested here, which can be accessed through the Digital Biomarker Discovery Pipeline as the “Biosignal Data Compression Toolbox,” an open-source, accessible software platform for compressing biosignal data.

scholarly journals A New Adaptive Method for the Extraction of Steel Design Structures from an Integrated Point Cloud

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3416
Author(s):  
Pawel Burdziakowski ◽  
Angelika Zakrzewska
Keyword(s):  
Data Integration ◽  
Point Cloud ◽  
Laser Scanning ◽  
Detection Threshold ◽  
Measurement Data ◽  
Steel Structures ◽  
Adaptive Method ◽  
Edge Extraction ◽  
Geometric Structures ◽  
Redundant Data

The continuous and intensive development of measurement technologies for reality modelling with appropriate data processing algorithms is currently being observed. The most popular methods include remote sensing techniques based on reflected-light digital cameras, and on active methods in which the device emits a beam. This research paper presents the process of data integration from terrestrial laser scanning (TLS) and image data from an unmanned aerial vehicle (UAV) that was aimed at the spatial mapping of a complicated steel structure, and a new automatic structure extraction method. We proposed an innovative method to minimize the data size and automatically extract a set of points (in the form of structural elements) that is vital from the perspective of engineering and comparative analyses. The outcome of the research was a complete technology for the acquisition of precise information with regard to complex and high steel structures. The developed technology includes such elements as a data integration method, a redundant data elimination method, integrated photogrammetric data filtration and a new adaptive method of structure edge extraction. In order to extract significant geometric structures, a new automatic and adaptive algorithm for edge extraction from a random point cloud was developed and presented herein. The proposed algorithm was tested using real measurement data. The developed algorithm is able to realistically reduce the amount of redundant data and correctly extract stable edges representing the geometric structures of a studied object without losing important data and information. The new algorithm automatically self-adapts to the received data. It does not require any pre-setting or initial parameters. The detection threshold is also adaptively selected based on the acquired data.

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