• Structural Monitoring and Maintenance
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• v.5 no.1
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• pp.51-65
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• 2018

A number of sensing techniques have been implemented for detecting defects in civil infrastructures instead of onsite human inspections in structural health monitoring. However, the issue of faults in sensors has not received much attention. This issue may lead to incorrect interpretation of data and false alarms. To overcome these challenges, this article presents a deep learning-based method with a new architecture of Stateful Long Short Term Memory Neural Networks (S-LSTM NN) for detecting sensor fault without going into details of the fault features. As LSTMs are capable of learning data features automatically, and the proposed method works without an accurate mathematical model. The detection of four types of sensor faults are studied in this paper. Non-stationary acceleration responses of a three-span continuous bridge when under operational conditions are studied. A deep network model is applied to the measured bridge data with estimation to detect the sensor fault. Another set of sensor output data is used to supervise the network parameters and backpropagation algorithm to fine tune the parameters to establish a deep self-coding network model. The response residuals between the true value and the predicted value of the deep S-LSTM network was statistically analyzed to determine the fault threshold of sensor. Experimental study with a cable-stayed bridge further indicated that the proposed method is robust in the detection of the sensor fault.

• Journal of Electrical Engineering and Technology
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• v.3 no.2
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• pp.213-217
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• 2008

The flux-lock type superconducting fault current limiter(SFCL) can apply the magnetic field into the high-$T_C$ superconducting(HTSC) element by adopting the magnetic field coil in its third winding. To apply the magnetic field into the HTSC element effectively, the capacitor for LC resonance is connected in series with the magnetic field coil. However, the current waveform of third winding for the application of the magnetic field is affected by the LC resonance condition for the frequency of the source voltage and can affect the waveform of the limited fault current. In this paper, the current waveform of the third winding in the flux-lock type SFCL according to LC resonance condition during a fault period was analyzed. From the differential equation for its electrical circuit, the current equation of the third winding was derived and described with the natural frequency and the damping ratio as design parameters. Through the analysis according to the design parameters of the third winding, the waveform of the limited fault current was confirmed to be influenced by the current waveform of the third winding and the design condition for the stable fault current limiting operation of this SFCL was obtained.

• Earthquakes and Structures
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• v.25 no.1
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• pp.15-26
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• 2023

It is of great importance to assess the residual displacement demand in the performance-based seismic design. In this paper, a hybrid deep learning model for predicting the residual displacement spectra under near-fault (NF) ground motions is proposed by combining the long short-term memory network (LSTM) and back-propagation (BP) network. The model is featured by its capacity of predicting the residual displacement spectrum under a given NF ground motion while considering the effects of structural parameters. To construct this model, 315 natural and artificial NF ground motions were employed to compute the residual displacement spectra through elastoplastic time history analysis considering different structural parameters. Based on the resulted dataset with a total of 9,450 samples, the proposed model was finally trained and tested. The results show that the proposed model has a satisfactory accuracy as well as a high efficiency in predicting residual displacement spectra under given NF ground motions while considering the impacts of structural parameters.

• Journal of Positioning, Navigation, and Timing
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• v.13 no.2
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• pp.189-197
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• 2024

Galileo is a European Global Navigation Satellite System (GNSS) that has offered the Galileo Open Service since 2016. Consequently, the standardization of GNSS augmentation systems, such as Satellite Based Augmentation System (SBAS), Ground Based Augmentation System (GBAS), and Aircraft Based Augmentation System (ABAS) for Galileo signals, is ongoing. In 2023, the European Union Space Programme Agency (EUSPA) released prior probabilities of a satellite fault and a constellation fault for Galileo, which are 3×10^-5 and 2×10^-4 per hour, respectively. In particular, the prior probability of a Galileo constellation fault is significantly higher than that for the GPS constellation fault, which is defined as 1×10^-8 per hour. This raised concerns about its potential impact on GBAS integrity monitoring. According to the Global Positioning System (GPS) Standard Positioning Service Performance Standard (SPS PS), a constellation fault is classified as a wide fault. A wide fault refers to a fault that affects more than two satellites due to a common cause. Such a fault can be caused by a failure in the Earth Orientation Parameter (EOP). The EOP is used when transforming the inertial axis, on which the orbit determination is based, to Earth Centered Earth Fixed (ECEF) axis, accounting for the irregularities in the rotation of the Earth. Therefore, a faulty EOP can introduce errors when computing a satellite position with respect to the ECEF axis. In GNSS, the ephemeris parameters are estimated based on the positions of satellites and are transmitted to navigation satellites. Subsequently, these ephemeris parameters are broadcasted via the navigation message to users. Therefore, a faulty EOP results in erroneous broadcast ephemeris data. In this paper, we assess the conventional ephemeris fault detection monitor currently employed in GBAS for wide faults, as current GBAS considers only single failure cases. In addition to the existing requirements defined in the standards on the Probability of Missed Detection (PMD), we derive a new PMD requirement tailored for a wide fault. The compliance of the current ephemeris monitor to the derived requirement is evaluated through a simulation. Our findings confirm that the conventional monitor meets the requirement even for wide fault scenarios.

• Proceedings of the Korea Society for Simulation Conference
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• 2001.10a
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• pp.301-305
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• 2001

A novel approach based on parameters estimation is presented far fault detection and diagnosis in sensors. Based on known precise parameter of normal working sensors system model is built from real laboratory inputs-outputs data, sequentially residual serial is obtained. Where decision-making rule of detection the fault is given via the use of beys theory, whilst a filter least-square computative algorithm for estimating fault parameters is given. The algorithm is a fast and accurate to calculate value of sensors faults when system model contains noise and sensors outputs contain measured noise. The method can solve both gain type and bias type fault in sensors. Simulated numerical example is included to demonstrate the use of the proposed approaches.

• International Journal of Air-Conditioning and Refrigeration
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• v.9 no.3
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• pp.63-71
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• 2001

This paper presents a model-based method for detecting and diagnosing some faults in the cooling tower of healing, ventilating, and air-conditioning systems. A simple model for the cooling tower is employed. Faults in cooling tower operation are detected through the deviations in the values of system characteristic parameters such as the heat transfer coefficient-area product, the tower approach, the tower effectiveness, and fan power. Three distinct faults are considered: cooling tower inlet water temperature sensor fault, cooling tower pump fault, and cooling tower fan fault. As a result, most values of the system characteristics parameter variations due to a fault are much higher or lower than the values without faults. This allows the faults in a cooling tower to be detected easily using above methods. The diagnostic rules for the faults were also developed through investigating the changes in the different parameter due to each faults.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.3
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• pp.135-139
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• 2004

Nowadays, one of the serious problems in KEPCO system is large fault current which exceeds the SCC(Short Circuit Capacity) of circuit breaker, As the superconductivity technology has been developed, the HTS-FCL(High Temperature Superconductor-Fault Current Limiter) can be one of the attractive alternatives to solve the fault current problem. However, the parameters of HTS-FCL should be designed optimally to have a best performance. Under this background, this paper presents the optimal design method of parameters for resistive type HTS-FCL using stochastic analysis technique.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.19 no.4
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• pp.533-546
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• 2021

Large earthquakes with (M_W > ~ 6) result in ground shaking, surface ruptures, and permanent deformation with displacement. The earthquakes would damage important facilities and infrastructure such as large industrial establishments, nuclear power plants, and waste disposal sites. In particular, earthquake ruptures associated with large earthquakes can affect geological and engineered barriers such as deep geological repositories that are used for storing hazardous radioactive wastes. Earthquake-driven faults and surface ruptures exhibit various fault zone structural characteristics such as direction of earthquake propagation and rupture and asymmetric displacement patterns. Therefore, estimating the respect distances and hazardous areas has been challenging. We propose that considering multiple parameters, such as fault types, distribution, scale, activity, linkage patterns, damage zones, and respect distances, enable accurate identification of the sites for deep geological repositories and important facilities. This information would enable earthquake hazard assessment and lower earthquake-resulted hazards in potential earthquake-prone areas.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.10
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• pp.1272-1278
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• 1999

A fault is considered as a variation of physical parameters; therefore the design of fault detection and identification(FDI) can be reduced to the parameter identification of a non linear system and to the association of the set of the estimated parameters with the mode of faults. Neuro-Fuzzy Inference System which contains multiple linear models as consequent part is used to model nonlinear systems. Generally, the linear parameters in neuro-fuzzy inference system can be effectively utilized to fault diagnosis. In this paper, we proposes an FDI system for nonlinear systems using neuro-fuzzy inference system. The proposed diagnostic system consists of two neuro-fuzzy inference systems which operate in two different modes (parallel and series-parallel mode). It generates the parameter residuals associated with each modes of faults which can be further processed by additional RBF (Radial Basis Function) network to identify the faults. The proposed FDI scheme has been tested by simulation on two-tank system.

• Smart Structures and Systems
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• v.13 no.3
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• pp.453-471
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• 2014

The fault diagnosis of rolling element bearings has drawn considerable research attention in recent years because these fundamental elements frequently suffer failures that could result in unexpected machine breakdowns. Artificial intelligence algorithms such as artificial neural networks (ANNs) and support vector machines (SVMs) have been widely investigated to identify various faults. However, as the useful life of a bearing deteriorates, identifying early bearing faults and evaluating their sizes of development are necessary for timely maintenance actions to prevent accidents. This study proposes a new two-layer structure consisting of support vector regression machines (SVRMs) to recognize bearing fault patterns and track the fault sizes. The statistical parameters used to track the fault evolutions are first extracted to condense original vibration signals into a few compact features. The extracted features are then used to train the proposed two-layer SVRMs structure. Once these parameters of the proposed two-layer SVRMs structure are determined, the features extracted from other vibration signals can be used to predict the unknown bearing health conditions. The effectiveness of the proposed method is validated by experimental datasets collected from a test rig. The results demonstrate that the proposed method is highly accurate in differentiating between fault patterns and determining their fault severities. Further, comparisons are performed to show that the proposed method is better than some existing methods.