• Smart Structures and Systems
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• v.31 no.2
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• pp.101-111
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• 2023

Structural health monitoring (SHM) covers a range of damage detection strategies for buildings. In real-time, SHM provides a basis for rapid decision making to optimise the speed and economic efficiency of post-event response. Previous work introduced an SHM method based on identifying structural nonlinear hysteretic parameters and their evolution from structural force-deformation hysteresis loops in real-time. This research extends and generalises this method to investigate the impact of a wide range of flag-shaped or pinching shape nonlinear hysteretic response and its impact on the SHM accuracy. A particular focus is plastic stiffness (K_p), where accurate identification of this parameter enables accurate identification of net and total plastic deformation and plastic energy dissipated, all of which are directly related to damage and infrequently assessed in SHM. A sensitivity study using a realistic seismic case study with known ground truth values investigates the impact of hysteresis loop shape, as well as added noise, on SHM accuracy using a suite of 20 ground motions from the PEER database. Monte Carlo analysis over 22,000 simulations with different hysteresis loops and added noise resulted in absolute percentage identification error (median, (IQR)) in K_p of 1.88% (0.79, 4.94)%. Errors were larger where five events (Earthquakes #1, 6, 9, 14) have very large errors over 100% for resulted K_p as an almost entirely linear response yielded only negligible plastic response, increasing identification error. The sensitivity analysis shows accuracy is reduces to within 3% when plastic drift is induced. This method shows clear potential to provide accurate, real-time metrics of non-linear stiffness and deformation to assist rapid damage assessment and decision making, utilising algorithms significantly simpler than previous non-linear structural model-based parameter identification SHM methods.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.4
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• pp.1-8
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• 2009

The proposed real-time structural health monitoring(SHM) system in past transferred and received data, central server gathered data from sensors, through coaxial cable. an immense sum of money is required to structure sensor network using coaxial cable. This paper proposes USN-based structural health monitoring(SHM). AIso, this paper designs and realizes prototypes according to proposed SHM. The value of sensing data obtained through HSDPA transfer to the BMS(Bridge Monitoring Server) passing through the TCP / IP socket by building two-way communication system, We have implemented a complete graph converting full system.

• Journal of Appropriate Technology
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• v.7 no.1
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• pp.26-32
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• 2021

In the process of vaccine delivery and vaccination, temperature is mostly controlled by an insulated containers containing ice. Moreover, amount of wasted vaccine is significant because the temperature of the vaccine is not properly controlled. A core challenge of vaccination is temperature data monitoring, since it is critical for managing and operating strategical vaccination by health organizations. In this research, a real-time monitoring vaccine carrier system was developed. Temperature, location, and power consumption data of the vaccine carrier were monitored and working performances of the vaccine carrier were tested in both Korea and Tanzania (Arusha and Kilimanjaro regions). For both places, Short Message Service (SMS) communication method was used to send information of the carrier's status. As a result, the monitoring system was able to transmit and receive real-time data of the vaccine carrier status while the vaccine carrier was tested. The vaccine status data can be accessed from any location through the cloud server and web-based user interface.

• Smart Structures and Systems
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• v.18 no.5
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• pp.955-982
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• 2016

Recent studies integrating vibration control and structural health monitoring (SHM) use control devices and control algorithms to enable system identification and damage detection. In this study real-time SHM is used to enhance structural vibration control and reduce damage. A newly proposed control algorithm, including integrated real-time SHM and semi-active control strategy, is presented to mitigate both damage and seismic response of the main structure under strong seismic ground motion. The semi-active independently variable stiffness (SAIVS) device is used as semi-active control device in this investigation. The proper stiffness of SAIVS device is obtained using a new developed semi-active control algorithm based on real-time damage tracking of structure by damage detection algorithm based on identified system Markov parameters (DDA/ISMP) method. A three bay five story steel braced frame structure, which is equipped with one SAIVS device at each story, is employed to illustrate the efficiency of the proposed algorithm. The obtained results show that the proposed control algorithm could significantly decrease damage in most parts of the structure. Also, the dynamic response of the structure is effectively reduced by using the proposed control algorithm during four strong earthquakes. In comparison to passive on and off cases, the results demonstrate that the performance of the proposed control algorithm in decreasing both damage and dynamic responses of structure is significantly enhanced than the passive cases. Furthermore, from the energy consumption point of view the maximum and the cumulative control force in the proposed control algorithm is less than the passive-on case, considerably.

• Journal of Environmental Science International
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• v.24 no.7
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• pp.875-882
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• 2015

In 2013, International Agency for Research on Cancer (IARC) concluded that outdoor air pollution is carcinogenic to humans, with the particulate matter component of air pollution most closely associated with sufficient evidence of increased cancer incidence by exposure to particulate matter component of air pollution. Motor vehicles are one of a major emission sources of fine particle ($PM_{2.5}$) in urban areas. A large number of epidemiological studies have reported a positive association of morbidity or mortality with distance from the roadside. We conducted this study to assess the association of $PM_{2.5}$ concentrations measured at roadside hotspots with those at adjacent residential sites using real-time $PM_{2.5}$ monitors. We conducted real-time $PM_{2.5}$ measurements for rush hour periods (08:00~10:00 and 18:00~20:00) at 9 roadside air monitoring Hotspot sites in metropolitan Seoul over 3 weeks from October 1 to 21, 2013. Simultaneous measurements were conducted in residential sites within a 100 m radius from each roadside air monitoring site. A SidePak AM510 was used for the real-time $PM_{2.5}$ measurements. Medians of roadside $PM_{2.5}$ concentrations ranged from $9.8{\mu}g/m^3$ to $38.3{\mu}g/m^3$, while corresponding median values at adjacent residential sites ranged from $4.4{\mu}g/m^3$ to $37.3{\mu}g/m^3$. $PM_{2.5}$ concentrations of residential sites were 0.97 times of hotspot roadside sites. Distributions of $PM_{2.5}$ concentrations in roadside and residential areas were also very similar. Real-time $PM_{2.5}$ concentrations at residential sites, (100 m adjacent), showed similar levels to those at roadside sites. Increasing the distance between roadside and residential sites, if needed, should be considered to protect urban resident populations from $PM_{2.5}$ emitted by traffic related sources.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.263-266
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• 2019

In this paper, we suggest an activity and health monitoring system to observe the status of the dogs in real time. We also propose a k-days algorithm which helps monitoring pet health status using classified activity data from a machine learning approach. One of the best machine learning algorithm is used for the classification activity of dogs. Dog health status is acquired by comparing current activity calculation with passed k-days activities average. It is considered as a good, warning and bad health status for differences between current and k-days summarized moving average (SMA) > 30, SMA between 30 and 50, and SMA < 50, respectively.

• Smart Structures and Systems
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• v.29 no.1
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• pp.129-140
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• 2022

The effectiveness of system identification, damage detection, condition assessment and other structural analyses relies heavily on the accuracy and reliability of the measured data in structural health monitoring (SHM) systems. However, data anomalies often occur in SHM systems, leading to inaccurate and untrustworthy analysis results. Therefore, anomalies in the raw data should be detected and cleansed before further analysis. Previous studies on data anomaly detection mainly focused on just single type of data anomaly for denoising or removing outliers, meanwhile, the existing methods of detecting multiple data anomalies are usually time consuming. For these reasons, recognising multiple anomaly patterns for real-time alarm and analysis in field monitoring remains a challenge. Aiming to achieve an efficient and accurate detection for multi-type data anomalies for field SHM, this study proposes a pattern-recognition-based data anomaly detection method that mainly consists of three steps: the feature extraction from the long time-series data samples, the training of a pattern recognition neural network (PRNN) using the features and finally the detection of data anomalies. The feature extraction step remarkably reduces the time cost of the network training, making the detection process very fast. The performance of the proposed method is verified on the basis of the SHM data of two practical long-span bridges. Results indicate that the proposed method recognises multiple data anomalies with very high accuracy and low calculation cost, demonstrating its applicability in field monitoring.

• Wind and Structures
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• v.21 no.6
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• pp.693-707
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• 2015

The efficiency of wind turbines (WT) is primarily reflected in their ability to generate electricity at any time. Downtimes of WTs due to "conventional" inspections are cost-intensive and undesirable for investors. For this reason, there is a need for structural health monitoring (SHM) systems, to enable service and maintenance on demand and to increase the inspection intervals. In general, monitoring increases the cost effectiveness of WTs. This publication concentrates on the application of two vibration-based SHM algorithms for stability and structural change monitoring of offshore WTs. Only data driven, output-only algorithms based on stochastic subspace identification (SSI) in time domain are considered. The centerpiece of this paper deals with the rough mathematical description of the dynamic behavior of offshore WTs and with the basic presentation of stochastic subspace-based algorithms and their application to these structures. Due to the early stage of the industrial application of SHM on offshore WT on the one side and the required confidentiality to the plant manufacturer and operator on the other side, up to now it is not possible to analyze different isolated structural damages resp. changes in a systematic manner, directly by means of in-situ measurement and to make these "acknowledgements" publicly available. For this reason, the sensitivity of the methods for monitoring purposes are demonstrated through their application on long time measurements from a 1:10 large scale test rig of an offshore WT under different conditions: undamaged, different levels of loosened bolt connections between tower parts, different levels of fouling, scouring and structure inclination. The limitation and further requirements for the approaches and their applicability on real foundations are discussed along the paper.

• International Journal of Aeronautical and Space Sciences
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• v.10 no.2
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• pp.34-42
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• 2009

Gas turbine performance diagnostics is a method for detecting, isolating and quantifying faults in gas turbine gas path components. On-line precise fault diagnosis can promote greatly reliability and availability of gas turbine in real time operation. This work proposes a GUI-type on-line diagnostic program using SIMULINK and Fuzzy-Neuro algorithms for a helicopter turboshaft engine. During development of the diagnostic program, a look-up table type base performance module are used for reducing computer calculating time and a signal generation module for simulating real time performance data. This program is composed of the on-line condition monitoring program to monitor on-line measuring performance condition, the fuzzy inference system to isolate the faults from measuring data and the neural network to quantify the isolated faults. Evaluation of the proposed on-line diagnostic program is performed through application to the helicopter engine health monitoring.

• Journal of the Korean Society for Nondestructive Testing
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• v.31 no.2
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• pp.165-173
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• 2011

The acoustic emission(AE) technique is a well established method to carry out structural health monitoring(SHM) of large structures. However, the real-time monitoring of the crack growth in the roller coaster support structures is not easy since the vehicle operation produces very large noise as well as crack growth. In this investigation, we present the waveform parameter filtering method to classify acoustic sources in real-time. This method filtrates only the AE hits by the target acoustic source as passing hits in a specific parameter band. According to various acoustic sources, the waveform parameters were measured and analyzed to verify the present filtering method. Also, the AE system employing the waveform parameter filter was manufactured and applied to the roller coaster support structure in an actual amusement park.