• Smart Structures and Systems
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• v.29 no.1
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• pp.53-62
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• 2022

The deployment of advanced structural health monitoring (SHM) systems in large-scale civil structures collects large amounts of data. Note that these data may contain multiple types of anomalies (e.g., missing, minor, outlier, etc.) caused by harsh environment, sensor faults, transfer omission and other factors. These anomalies seriously affect the evaluation of structural performance. Therefore, the effective analysis and mining of SHM data is an extremely important task. Inspired by the deep learning paradigm, this study develops a novel generative adversarial network (GAN) and convolutional neural network (CNN)-based data anomaly detection approach for SHM. The framework of the proposed approach includes three modules : (a) A three-channel input is established based on fast Fourier transform (FFT) and Gramian angular field (GAF) method; (b) A GANomaly is introduced and trained to extract features from normal samples alone for class-imbalanced problems; (c) Based on the output of GANomaly, a CNN is employed to distinguish the types of anomalies. In addition, a dataset-oriented method (i.e., multistage sampling) is adopted to obtain the optimal sampling ratios between all different samples. The proposed approach is tested with acceleration data from an SHM system of a long-span bridge. The results show that the proposed approach has a higher accuracy in detecting the multi-pattern anomalies of SHM data.

• Journal of the Korean Society for Precision Engineering
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• v.30 no.5
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• pp.480-485
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• 2013

Enhancing energy efficiency of machine tools causes substantial impacts on the manufacturing industries, to cope with the competitive introduction of the total energy management strategies. Real-time energy monitoring is essential to identify energy consumption patterns of the machine tools and correlate them with the energy management strategy. Integrated analysis of machine tool's operation status and the corresponding energy usage is most important to accurately evaluate the energy efficiency under the various machining process environments. This paper proposes a system architecture to realize the online energy monitoring system and the embedded energy monitoring approach interconnected with the CNC kernel. The shop-floor operation management system is presented to integrate the proposed online energy monitoring approach.

• Earthquakes and Structures
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• v.11 no.6
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• pp.1143-1164
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• 2016

Time Delay of Arrival (TDOA) estimation methods based on correlation function analysis play an important role in the micro-seismic event monitoring. It makes full use of the similarity in the recorded signals that are from the same source. However, those methods are subjected to the noise effect, particularly when the global similarity of the signals is low. This paper proposes a new approach for micro-seismic monitoring based on cross wavelet transform. The cross wavelet transform is utilized to analyse the measured signals under micro-seismic events, and the cross wavelet power spectrum is used to measure the similarity of two signals in a multi-scale dimension and subsequently identify TDOA. The offset time instant associated with the maximum cross wavelet transform spectrum power is identified as TDOA, and then the location of micro-seismic event can be identified. Individual and statistical identification tests are performed with measurement data from an in-field mine. Experimental studies demonstrate that the proposed approach significantly improves the robustness and accuracy of micro-seismic source locating in mines compared to several existing methods, such as the cross-correlation, multi-correlation, STA/LTA and Kurtosis methods.

• Computers and Concrete
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• v.20 no.5
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• pp.555-562
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• 2017

The accuracy and integrity of stress data acquired by bridge heath monitoring system is of significant importance for bridge safety assessment. However, the missing and abnormal data are inevitably existed in a realistic monitoring system. This paper presents a data reconstruction approach for bridge heath monitoring based on the wavelet multi-resolution analysis and support vector machine (SVM). The proposed method has been applied for data imputation based on the recorded data by the structural health monitoring (SHM) system instrumented on a prestressed concrete cable-stayed bridge. The effectiveness and accuracy of the proposed wavelet-based SVM prediction method is examined by comparing with the traditional autoregression moving average (ARMA) method and SVM prediction method without wavelet multi-resolution analysis in accordance with the prediction errors. The data reconstruction analysis based on 5-day and 1-day continuous stress history data with obvious preternatural signals is performed to examine the effect of sample size on the accuracy of data reconstruction. The results indicate that the proposed data reconstruction approach based on wavelet multi-resolution analysis and SVM is an effective tool for missing data imputation or preternatural signal replacement, which can serve as a solid foundation for the purpose of accurately evaluating the safety of bridge structures.

• Structural Engineering and Mechanics
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• v.73 no.3
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• pp.331-337
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• 2020

This paper proposes a new approximate analytical solution for highly nonlinear vibration of mechanical systems called Hamiltonian Approach (HA) that can be widely use for structural health monitoring systems. The complete procedure of the HA approach is studied, and the precise application of the presented approach is surveyed by two familiar nonlinear partial differential problems. The nonlinear frequency of the considered systems is obtained. The results of the HA are verified with the numerical solution using Runge-Kutta's [RK] algorithm. It is established the only one iteration of the HA leads us to the high accurateness of the solution.

• Structural Monitoring and Maintenance
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• v.3 no.3
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• pp.215-232
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• 2016

This paper presents a multi helical ultrasonic imaging approach for quantitative corrosion damage monitoring of cylindrical structures. The approach consists of two stages. First a multi helical ultrasonic imaging (MHUI) algorithm is used to provide qualitative images of the structure of interest. Then, an optimization problem is solved in order to obtain quantitative damage information, such as thickness map. Experimental tests are carried out on a steel pipe instrumented with six piezoelectric transducers to validate the proposed approach. Three thickness recesses are considered to simulate corrosion damage. The results show the efficiency of the proposed approach for quantifying corrosion location, area and remnant thickness.

• Structural Monitoring and Maintenance
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• v.4 no.4
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• pp.381-396
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• 2017

Structural health monitoring (SHM) is a necessity for reliable and efficient functioning of engineering systems. Damage detection (DD) is a crucial component of any SHM system. Lamb waves are a popular means to DD owing to their sensitivity to small damages over a substantial length. This typically involves an active sensing paradigm in a pitch-catch setting, that involves two piezo-sensors, a transmitter and a receiver. In this paper, we propose a data-intensive DD approach for beam structures using high frequency signals acquired from beams in a pitch-catch setting. The key idea is to develop a statistical learning-based approach, that harnesses the inherent sparsity in the problem. The proposed approach performs damage detection, localization in beams. In addition, quantification is possible too with prior calibration. We demonstrate numerically that the proposed approach achieves 100% accuracy in detection and localization even with a signal to noise ratio of 25 dB.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3946-3965
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• 2018

Network anomaly detection in Software Defined Networking, especially the detection of DDoS attack, has been given great attention in recent years. It is convenient to build the Traffic Matrix from a global view in SDN. However, the monitoring and management of high-volume feature-rich traffic in large networks brings significant challenges. In this paper, we propose a moving window Principal Components Analysis based anomaly detection and mitigation approach to map data onto a low-dimensional subspace and keep monitoring the network state in real-time. Once the anomaly is detected, the controller will install the defense flow table rules onto the corresponding data plane switches to mitigate the attack. Furthermore, we evaluate our approach with experiments. The Receiver Operating Characteristic curves show that our approach performs well in both detection probability and false alarm probability compared with the entropy-based approach. In addition, the mitigation effect is impressive that our approach can prevent most of the attacking traffic. At last, we evaluate the overhead of the system, including the detection delay and utilization of CPU, which is not excessive. Our anomaly detection approach is lightweight and effective.

• Smart Structures and Systems
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• v.19 no.6
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• pp.587-599
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• 2017

This paper introduces an approach to the realization of an ICT-based bridge remote monitoring system that enables real-time monitoring and controlled adjustments for unexpected heavy loads and also for damaging earthquakes or typhoons. In this paper, an integrated bridge remote monitoring system called the "Intelligent Bridge", which consists of a stand-alone monitoring system (SMS) and a web-based Internet monitoring system(IMS), was developed for not only bridge maintenance but also as an application for a para-stressing bridge system. To verify the possibility of controlling the actual structural performance of an "Intelligent Bridge", a model 2-span continuous cable-stayed bridge with adjustable cables was constructed. The experimental results demonstrate that the implemented monitoring system supplies detailed and accurate information about bridge behaviour for further evaluation and diagnosis, and it also opens up prospects for future application of a web-based remote system to actually adjust in-service bridges under field conditions.

• Structural Engineering and Mechanics
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• v.63 no.6
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• pp.809-824
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• 2017

The accurate evaluation of wind characteristics and wind-induced structural responses during a typhoon is of significant importance for bridge design and safety assessment. This paper presents an expectation maximization (EM) algorithm-based angular-linear approach for probabilistic modeling of field-measured wind characteristics. The proposed method has been applied to model the wind speed and direction data during typhoons recorded by the structural health monitoring (SHM) system instrumented on the arch Jiubao Bridge located in Hangzhou, China. In the summer of 2015, three typhoons, i.e., Typhoon Chan-hom, Typhoon Soudelor and Typhoon Goni, made landfall in the east of China and then struck the Jiubao Bridge. By analyzing the wind monitoring data such as the wind speed and direction measured by three anemometers during typhoons, the wind characteristics during typhoons are derived, including the average wind speed and direction, turbulence intensity, gust factor, turbulence integral scale, and power spectral density (PSD). An EM algorithm-based angular-linear modeling approach is proposed for modeling the joint distribution of the wind speed and direction. For the marginal distribution of the wind speed, the finite mixture of two-parameter Weibull distribution is employed, and the finite mixture of von Mises distribution is used to represent the wind direction. The parameters of each distribution model are estimated by use of the EM algorithm, and the optimal model is determined by the values of $R^2$ statistic and the Akaike's information criterion (AIC). The results indicate that the stochastic properties of the wind field around the bridge site during typhoons are effectively characterized by the proposed EM algorithm-based angular-linear modeling approach. The formulated joint distribution of the wind speed and direction can serve as a solid foundation for the purpose of accurately evaluating the typhoon-induced fatigue damage of long-span bridges.