• Structural Engineering and Mechanics
• /
• 제63권6호
• /
• pp.779-788
• /
• 2017

System identification and damage detection for structural health monitoring have received considerable attention. Various time domain analysis methodologies based on measured vibration data of structures have been proposed. Among them, recursive least-squares estimation of structural parameters which is also known as parametric Kalman filter (PKF) approach has been studied. However, the conventional PKF requires that all the external excitations (inputs) be available. On the other hand, structural uncertainties are inevitable for civil infrastructures, it is necessary to develop approaches for probabilistic damage detection of structures. In this paper, a parametric Kalman filter with unknown inputs (PKF-UI) is proposed for the simultaneous identification of structural parameters and the unmeasured external inputs. Analytical recursive formulations of the proposed PKF-UI are derived based on the conventional PKF. Two scenarios of linear observation equations and nonlinear observation equations are discussed, respectively. Such a straightforward derivation of PKF-UI is not available in the literature. Then, the proposed PKF-UI is utilized for probabilistic damage detection of structures by considering the uncertainties of structural parameters. Structural damage index and the damage probability are derived from the statistical values of the identified structural parameters of intact and damaged structure. Some numerical examples are used to validate the proposed method.

• Smart Structures and Systems
• /
• 제28권6호
• /
• pp.811-825
• /
• 2021

High-voltage isolating switches play a paramount role in ensuring the safety of power supply systems. However, their exposure to outdoor environmental conditions may cause serious physical defects, which may result in great risk to power supply systems and society. Image processing-based methods have been used for anomaly detection. However, their accuracy is affected by numerous uncertainties due to manually extracted features, which makes the anomaly detection of isolating switches still challenging. In this paper, a vision-based anomaly detection method for isolating switches, which uses the rotational angle of the switch system for more accurate and direct anomaly detection with the help of deep learning (DL) and image processing methods (Single Shot Multibox Detector (SSD), improved frame differencing method, and Hough transform), is proposed. The SSD is a deep learning method for object classification and localization. In addition, an improved frame differencing method is introduced for better feature extraction and a hough transform method is adopted for rotational angle calculation. A number of experiments are conducted for anomaly detection of single and multiple switches using video frames. The results of the experiments demonstrate that the SSD outperforms the You-Only-Look-Once network. The effectiveness and robustness of the proposed method have been proven under various conditions, such as different illumination and camera locations using 96 videos from the experiments.

• Smart Structures and Systems
• /
• 제21권4호
• /
• pp.435-448
• /
• 2018

This paper reports the development of a theoretically rigorous method for permanent way engineers to assess the condition of railway ballast under a concrete sleeper with the potential to be extended to a smart system for long-term health monitoring of railway ballast. Owing to the uncertainties induced by the problems of modeling error and measurement noise, the Bayesian approach was followed in the development. After the selection of the most plausible model class for describing the damage status of the rail-sleeper-ballast system, Bayesian model updating is adopted to calculate the posterior PDF of the ballast stiffness at various regions under the sleeper. An obvious drop in ballast stiffness at a region under the sleeper is an evidence of ballast damage. In model updating, the model that can minimize the discrepancy between the measured and model-predicted modal parameters can be considered as the most probable model for calculating the posterior PDF under the Bayesian framework. To address the problems of non-uniqueness and local minima in the model updating process, a two-stage hybrid optimization method was developed. The modified evolutionary algorithm was developed in the first stage to identify the important regions in the parameter space and resulting in a set of initial trials for deterministic optimization to locate all most probable models in the second stage. The proposed methodology was numerically and experimentally verified. Using the identified model, a series of comprehensive numerical case studies was carried out to investigate the effects of data quantity and quality on the results of ballast damage detection. Difficulties to be overcome before the proposed method can be extended to a long-term ballast monitoring system are discussed in the conclusion.

• Structural Monitoring and Maintenance
• /
• 제8권4호
• /
• pp.379-402
• /
• 2021

Traditional approaches for structural health monitoring (SHM) seldom take ambient uncertainty (temperature, humidity, ambient vibration) into consideration, while their impacts on structural responses are substantial, leading to a possibility of raising false alarms. A few predictors model-based approaches deal with these uncertainties through complex numerical models running online, rendering the SHM approach to be compute-intensive, slow, and sometimes not practical. Also, with model-based approaches, the imperative need for a precise understanding of the structure often poses a problem for not so well understood complex systems. The present study employs a data-based approach coupled with Empirical mode decomposition (EMD) to correlate recorded response time histories under varying temperature conditions to corresponding damage scenarios. EMD decomposes the response signal into a finite set of intrinsic mode functions (IMFs). A two-dimensional Convolutional Neural Network (2DCNN) is further trained to associate these IMFs to the respective damage cases. The use of IMFs in place of raw signals helps to reduce the impact of sensor noise while preserving the essential spatio-temporal information less-sensitive to thermal effects and thereby stands as a better damage-sensitive feature than the raw signal itself. The proposed algorithm is numerically tested on a single span bridge under varying temperature conditions for different damage severities. The dynamic strain is recorded as the response since they are frame-invariant and cheaper to install. The proposed algorithm has been observed to be damage sensitive as well as sufficiently robust against measurement noise.

• Journal of Communications and Networks
• /
• 제14권3호
• /
• pp.225-229
• /
• 2012

We examine spectrum sensing in a situation of uncertain channel model. In particular, we assume that, besides additive noise, the observed signal contains an interference term whose probability distribution is unknown, and only its range and maximum power are known. We discuss the evaluation of the detector performance and its design in this situation. Although this paper specifically deals with the design of spectrum sensors, its scope is wider, as the applicability of its results extends to a general class of problems that may arise in the design of receivers whenever there is uncertainty about how to model the environment in which one is expected to operate. The theory expounded here allows one to determine the performance of a receiver, by combining the available (objective) probabilistic information with (subjective) information describing the designer's attitude.

• Nuclear Engineering and Technology
• /
• 제54권6호
• /
• pp.2213-2220
• /
• 2022

The maximum dose delivery at the end of the beam range provides the main advantage of using proton therapy. The range of the proton beam, however, is subject to uncertainties, which limit the clinical benefits of proton therapy and, therefore, accurate in vivo verification of the beam range is desirable. For the beam range verification in spot scanning proton therapy, a prompt gamma detection system, called as gamma electron vertex imaging (GEVI) system, is under development and, in the present study, the performance of the GEVI system in spot scanning proton therapy was predicted with Geant4 Monte Carlo simulations in terms of shift detection sensitivity, accuracy and precision. The simulation results indicated that the GEVI system can detect the interfractional range shifts down to 1 mm shift for the cases considered in the present study. The results also showed that both the evaluated accuracy and precision were less than 1-2 mm, except for the scenarios where we consider all spots in the energy layer for a local shifting. It was very encouraging results that the accuracy and precision satisfied the smallest distal safety margin of the investigated beam energy (i.e., 4.88 mm for 134.9 MeV).

• Smart Structures and Systems
• /
• 제20권4호
• /
• pp.427-440
• /
• 2017

One of the suitable structural damage detection methods using vibrational characteristics are damage-index-based methods. In this study, a damage index for identifying damages in plate structures using frequency response function (FRF) data has been provided. One of the significant challenges of identifying the damages in plate structures is high number of degrees of freedom resulting in decreased damage identifying accuracy. On the other hand, FRF data are of high volume and this dramatically decreases the computing speed and increases the memory necessary to store the data, which makes the use of this method difficult. In this study, FRF data are compressed using two-dimensional principal component analysis (2D-PCA), and then converted into damage index vectors. The damage indices, each of which represents a specific condition of intact or damaged structures are stored in a database. After computing damage index of structure with unknown damage and using algorithm of lookup tables, the structural damage including the severity and location of the damage will be identified. In this study, damage detection accuracy using the proposed damage index in square-shaped structural plates with dimensions of 3, 7 and 10 meters and with boundary conditions of four simply supported edges (4S), three clamped edges (3C), and four clamped edges (4C) under various single and multiple-element damage scenarios have been studied. Furthermore, in order to model uncertainties of measurement, insensitivity of this method to noises in the data measured by applying values of 5, 10, 15 and 20 percent of normal Gaussian noise to FRF values is discussed.

• International Journal of Automotive Technology
• /
• 제8권5호
• /
• pp.637-650
• /
• 2007

A new on-line fault detection and isolation(FDI) scheme has been proposed for engines using an adaptive neural network classifier; this paper investigates the robustness of this scheme by evaluating in a wide range of operational modes. The neural classifier is made adaptive to cope with the significant parameter uncertainty, disturbances, and environmental changes. The developed scheme is capable of diagnosing faults in the on-line mode and can be directly implemented in an on-board diagnosis system(hardware). The robustness of the FDI for the closed-loop system with crankshaft speed feedback is investigated by testing it for a wide range of operational modes, including robustness against fixed and sinusoidal throttle angle inputs, change in load, change in an engine parameter, and all changes occurring simultaneously. The evaluations are performed using a mean value engine model(MVEM), which is a widely used benchmark model for engine control system and FDI system design. The simulation results confirm the robustness of the proposed method for various uncertainties and disturbances.

• Fisheries and Aquatic Sciences
• /
• 제16권3호
• /
• pp.159-164
• /
• 2013

The mouse bioassay and high performance liquid chromatography (HPLC) post-column oxidation method are different methods of quantifying paralytic shellfish poisoning toxins. In this study, we compared their ability to accurately quantify the toxicity levels in two types of field sample (oysters and mussels) with different toxin profiles for routine regulatory monitoring. A total of 72 samples were analyzed by both methods, 44 of which gave negative results, with readings under the limit of detection of the mouse bioassay ($40{\mu}g/100g$ saxitoxin [STX] eq). In 14 oysters, the major toxin components were gonyautoxin (GTX) 1, -2, -3, -4, -5, decarbamoylgonyautoxin-2 (dcGTX2), and decarbamoylsaxitoxin (dcSTX), while 14 mussels tested positive for dcSTX, GTX2, -3, -4, -5, dcGTX2, neosaxitoxin (NEO), STX, and dcSTX. When the results obtained by both methods were compared in two matrices, a better correlation ($r^2=0.9478$) was obtained for mussels than for oysters ($r^2=0.8244$). Additional studies are therefore needed in oysters to investigate the differences in the results obtained by both methods. Importantly, some samples with toxin levels around the legal limit gave inconsistent results using HPLC-based techniques, which could have a strong economic impact due to enforced harvest area closure. It should therefore be determined if all paralytic shellfish poisoning toxins can be quantified accurately by HPLC, and if the uncertainties of the method lead to doubts regarding regulatory limits.

• 해양환경안전학회지
• /
• 제25권5호
• /
• pp.550-559
• /
• 2019

2014년 4월 발생한 세월호 침몰사고는 단순한 해양사고를 넘어 해양재난으로, 나아가 국민의 정서와 사회의 건전성까지 황폐화시킨 사회적 참사로 확대되었다. 따라서, 국가 운영에 치명적 영향을 미칠 수도 있는 대규모 선박사고나 해양오염사고, 그리고 자연재해 등 각종 해양재난에 대해 국가적 차원에서 철저한 대비 대응이 필요하다. 본 연구에서는 대규모 해양재난으로 인해 발생할 수 있는 국가 경제적 사회적 피해를 최소화하기 위해 국가적 위기를 불확실성에 근거하여 해석한 '검은 백조 이론'을 중심으로 대규모 해양재난에 대한 국가적 대응전략을 검토한다. 먼저, 사고예방을 위한 각 방어장벽별 결함을 최소화 시키는 노력과 함께 특정 방어장벽에 결함이 발생하더라도 그 결함이 위기사태로 연결되지 않도록 '해양재난의 검은 백조 탐지시스템'을 구축하는 한편, 해양재난을 관리하는 주관기관을 일원화하여 해양안전관리 전 분야를 체계적으로 관리하고, 국가적 해양재난대응 현장지휘 및 협업체계를 구축하여 사고현장에 투입된 모든 대응세력들이 현장지휘관의 지휘통제에 따라 단일조직의 구성요소처럼 일사불란하게 움직여 사고수습에 효과적으로 대응할 수 있도록 한다.