The Experts below are selected from a list of 297 Experts worldwide ranked by ideXlab platform
S D Fassois - One of the best experts on this subject based on the ideXlab platform.
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vibration based damage detection for a population of nominally identical structures unsupervised multiple model mm Statistical Time series type methods
Mechanical Systems and Signal Processing, 2018Co-Authors: Kyriakos J Vamvoudakisstefanou, John S. Sakellariou, S D FassoisAbstract:Abstract The problem of vibration-based damage detection for a population of nominally identical structures is considered via unsupervised Statistical Time series type methods. For this purpose a population sample comprising 31 nominally identical composite beams with significant beam-to-beam variability in the dynamics is employed, with impact-induced damage at various positions and two distinct energy levels. Two Multiple Model, MM, based Statistical Time series type methods are postulated, assessed, and compared with two ‘conventional’ methods. The assessment is based on a comprehensive and systematic procedure, making use of thousands of test cases via a ‘rotation’ procedure, with the results presented in the form of Receiver Operating Characteristic, ROC, curves. These indicate that ‘conventional’ methods are mostly ineffective, especially with low impact energy damages. On the other hand, the postulated Multiple Model parameter based methods achieve significantly improved performance, characterized as very good and providing overall correct damage detection rates approaching 100 % for false alarm rates at or above 5 % .
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a functional model based Statistical Time series method for vibration based damage detection localization and magnitude estimation
Mechanical Systems and Signal Processing, 2013Co-Authors: Fotis Kopsaftopoulos, S D FassoisAbstract:Abstract A vibration based Statistical Time series method that is capable of effective damage detection, precise localization, and magnitude estimation within a unified stochastic framework is introduced. The method constitutes an important generalization of the recently introduced functional model based method (FMBM) in that it allows for precise damage localization over properly defined continuous topologies (instead of pre-defined specific locations) and magnitude estimation for the first Time within the context of Statistical Time series methods that use partial identified models and a limited number of measured signals. Estimator uncertainties are taken into account, and uncertainty ellipsoids are provided for the damage location and magnitude. The method is based on the extended class of vector-dependent functionally pooled (VFP) models, which are characterized by parameters that depend on both damage magnitude and location, as well as on proper Statistical estimation and decision making schemes. The method is validated and its effectiveness is experimentally assessed via a proof-of-concept application to damage detection, precise localization, and magnitude estimation on a prototype GARTEUR-type laboratory scale aircraft skeleton structure. The damage scenarios consist of varying size small masses attached to various continuous topologies on the structure. The method is shown to achieve effective damage detection, precise localization, and magnitude estimation based on even a single pair of measured excitation–response vibration signals.
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vibration based health monitoring for a lightweight truss structure experimental assessment of several Statistical Time series methods
Mechanical Systems and Signal Processing, 2010Co-Authors: Fotis Kopsaftopoulos, S D FassoisAbstract:An experimental assessment of several vibration based Statistical Time series methods for Structural Health Monitoring (SHM) is presented via their application to a lightweight aluminum truss structure. A concise overview of the main non‐parametric and parametric methods is provided, including response‐only and excitation‐response schemes. Damage detection and identification is based on univariate (scalar) versions of the methods, while results for three distinct vibration mea surement positions on the structure are presented. The methods’ effectiveness is assessed via multiple experi ments under various damage scenarios. The results of the study confirm the high potential and effectiveness of s tatistical Time series methods for SHM.
Fotis Kopsaftopoulos - One of the best experts on this subject based on the ideXlab platform.
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Statistical Time Series Methods for Vibration Based Structural Health Monitoring
New Trends in Structural Health Monitoring, 2020Co-Authors: Spilios D. Fassois, Fotis KopsaftopoulosAbstract:Statistical Time series methods for vibration based structural health monitoring utilize random excitation and/or vibration response signals, Statistical model building, and Statistical decision making for inferring the health state of a structure. This includes damage detection, identification (including localization) and quantification. The principles and operation of methods that utilize the Time or frequency domains are explained, and they are classified into various categories under the broad non-parametric and parametric classes. Representative methods from each category are outlined and their use is illustrated via their application to a laboratory truss structure.
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A SEQUENTIAL Statistical Time SERIES FRAMEWORK FOR VIBRATION BASED STRUCTURAL HEALTH MONITORING
2020Co-Authors: Fotis Kopsaftopoulos, Spilios D. FassoisAbstract:The goal of this study is the introduction and experimental assessment of a Sequential Probability Ratio Test (SPRT) framework for vibration based Structural Health Monitoring (SHM). This employs the residual sequences obtained using a single stochastic Time series model of the healthy structure and is based on a combination of binary and multihypothesis versions of the SPRT. The framework’s performance is predetermined via the use of the Operating Characteristic (OC) and Average Sample Number (ASN) functions in combination with baseline experiments, while it requires on average a minimum number of samples in order to reach a decision compared to Fixed Sample Size (FSS) most powerful tests. The effectiveness of the proposed approach is validated and experimentally assessed via its application to a lightweight aluminum truss structure.
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a functional model based Statistical Time series method for vibration based damage detection localization and magnitude estimation
Mechanical Systems and Signal Processing, 2013Co-Authors: Fotis Kopsaftopoulos, S D FassoisAbstract:Abstract A vibration based Statistical Time series method that is capable of effective damage detection, precise localization, and magnitude estimation within a unified stochastic framework is introduced. The method constitutes an important generalization of the recently introduced functional model based method (FMBM) in that it allows for precise damage localization over properly defined continuous topologies (instead of pre-defined specific locations) and magnitude estimation for the first Time within the context of Statistical Time series methods that use partial identified models and a limited number of measured signals. Estimator uncertainties are taken into account, and uncertainty ellipsoids are provided for the damage location and magnitude. The method is based on the extended class of vector-dependent functionally pooled (VFP) models, which are characterized by parameters that depend on both damage magnitude and location, as well as on proper Statistical estimation and decision making schemes. The method is validated and its effectiveness is experimentally assessed via a proof-of-concept application to damage detection, precise localization, and magnitude estimation on a prototype GARTEUR-type laboratory scale aircraft skeleton structure. The damage scenarios consist of varying size small masses attached to various continuous topologies on the structure. The method is shown to achieve effective damage detection, precise localization, and magnitude estimation based on even a single pair of measured excitation–response vibration signals.
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Statistical Time series methods for damage diagnosis in a scale aircraft skeleton structure: loosened bolts damage scenarios
Journal of Physics: Conference Series, 2011Co-Authors: Fotis Kopsaftopoulos, Spilios D. FassoisAbstract:A comparative assessment of several vibration based Statistical Time series methods for Structural Health Monitoring (SHM) is presented via their application to a scale aircraft skeleton laboratory structure. A brief overview of the methods, which are either scalar or vector type, non-parametric or parametric, and pertain to either the response-only or excitation-response cases, is provided. Damage diagnosis, including both the detection and identification subproblems, is tackled via scalar or vector vibration signals. The methods' effectiveness is assessed via repeated experiments under various damage scenarios, with each scenario corresponding to the loosening of one or more selected bolts. The results of the study confirm the "global" damage detection capability and effectiveness of Statistical Time series methods for SHM.
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vibration based health monitoring for a lightweight truss structure experimental assessment of several Statistical Time series methods
Mechanical Systems and Signal Processing, 2010Co-Authors: Fotis Kopsaftopoulos, S D FassoisAbstract:An experimental assessment of several vibration based Statistical Time series methods for Structural Health Monitoring (SHM) is presented via their application to a lightweight aluminum truss structure. A concise overview of the main non‐parametric and parametric methods is provided, including response‐only and excitation‐response schemes. Damage detection and identification is based on univariate (scalar) versions of the methods, while results for three distinct vibration mea surement positions on the structure are presented. The methods’ effectiveness is assessed via multiple experi ments under various damage scenarios. The results of the study confirm the high potential and effectiveness of s tatistical Time series methods for SHM.
Spilios D. Fassois - One of the best experts on this subject based on the ideXlab platform.
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Statistical Time Series Methods for Vibration Based Structural Health Monitoring
New Trends in Structural Health Monitoring, 2020Co-Authors: Spilios D. Fassois, Fotis KopsaftopoulosAbstract:Statistical Time series methods for vibration based structural health monitoring utilize random excitation and/or vibration response signals, Statistical model building, and Statistical decision making for inferring the health state of a structure. This includes damage detection, identification (including localization) and quantification. The principles and operation of methods that utilize the Time or frequency domains are explained, and they are classified into various categories under the broad non-parametric and parametric classes. Representative methods from each category are outlined and their use is illustrated via their application to a laboratory truss structure.
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Statistical Time Series Methods for Structural Health Monitoring ∗ (Encyclopedia of Structural Health Monitoring, John Wiley & Sons: Contribution ID shm044)
2020Co-Authors: Spilios D. Fassois, John S. SakellariouAbstract:Statistical Time series methods for structural health monitoring utilize random e xcitation and/or vibration response signals, Statistical model building, and Statistical decision making fo r inferring the health state of a structure. This includes fault detection, identification (localization), as well as ma gnitude estimation. The principles and operation of methods that utilize the Time or frequency domains on a periodic inspection basis are explained, and the methods are classified into various categories under the general non‐parametric and parametric classes. Representative methods from each category are outlined and their use is illustrated on a laboratory structure.
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A SEQUENTIAL Statistical Time SERIES FRAMEWORK FOR VIBRATION BASED STRUCTURAL HEALTH MONITORING
2020Co-Authors: Fotis Kopsaftopoulos, Spilios D. FassoisAbstract:The goal of this study is the introduction and experimental assessment of a Sequential Probability Ratio Test (SPRT) framework for vibration based Structural Health Monitoring (SHM). This employs the residual sequences obtained using a single stochastic Time series model of the healthy structure and is based on a combination of binary and multihypothesis versions of the SPRT. The framework’s performance is predetermined via the use of the Operating Characteristic (OC) and Average Sample Number (ASN) functions in combination with baseline experiments, while it requires on average a minimum number of samples in order to reach a decision compared to Fixed Sample Size (FSS) most powerful tests. The effectiveness of the proposed approach is validated and experimentally assessed via its application to a lightweight aluminum truss structure.
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Vibration-response-only Statistical Time series structural health monitoring methods: A comprehensive assessment via a scale jacket structure:
Structural Health Monitoring-an International Journal, 2019Co-Authors: Nikos A Spanos, John S. Sakellariou, Spilios D. FassoisAbstract:Random-vibration-based Statistical Time series structural health monitoring methods utilize small-scale, compact, and data-based, Time series stochastic representations of the structural dynamics f...
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OUTPUT-ONLY Statistical Time SERIES METHODS FOR STRUCTURAL HEALTH MONITORING: A COMPARATIVE STUDY
2014Co-Authors: Kyriakos J. Vamvoudakis-stefanou, John S. Sakellariou, Spilios D. FassoisAbstract:A comparative assessment of six well known Output-Only Statistical Time Series Methods (OO-STSMs) for Structural Health Monitoring (SHM) is presented via damage detection and identification in a GARTEUR type aircraft skeleton structure. A concise overview of the methods highlighting their principles is presented and their effectiveness for damage detection and identification is assessed via numerous experiments and various damage cases on the skeleton structure. What is more, issues such as the methods effectiveness based on local or remote vibration sensors as well as their computational complexity and ease of use are also investigated.
Fumio Ishizaki - One of the best experts on this subject based on the ideXlab platform.
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Analysis of the Statistical Time-access fairness index of one-bit feedback fair scheduler
Numerical Algebra Control and Optimization, 2011Co-Authors: Fumio IshizakiAbstract:Recently various schedulers exploiting multiuser diversity in wireless networks have been proposed and studied. Although the utilization of multiuser diversity can increase the information theoretic capacity, there exists a tradeoff between the capacity and fairness. Among schedulers exploiting multiuser diversity, the one-bit feedback fair scheduler is considered as an attractive choice for the reduction of feedback overheads and the ease of implementation. In this paper, we study the short term fairness of the one-bit feedback fair scheduler. Since the short term fairness has a strong impact on the quality-of-service of each mobile station, it is important to examine the short term fairness properties of the scheduler. As a short term fairness index, we consider the Statistical Time-access fairness index (STAFI). We then develop two numerical methods to estimate the STAFI of the scheduler. The first method calculates the exact value of the STAFI by using the inverse discrete FFT method. The second method estimates the asymptotic decay rate of the STAFI by using the theory of large deviations. Numerical results show that the threshold value of the one-bit feedback fair scheduler greatly affects its short term fairness properties.
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Statistical Time-access fairness index of one-bit feedback fair scheduler
Proceedings of the 6th International Conference on Queueing Theory and Network Applications - QTNA '11, 2011Co-Authors: Fumio IshizakiAbstract:Since the utilization of multiuser diversity in wireless networks can increase the information theoretic capacity, much attention has been paid to schedulers exploiting multiuser diversity. It is known that there exists a tradeoff between the capacity and fairness achieved by schedulers exploiting multiuser diversity. Due to its good balance between the capacity and fairness, the one-bit feedback fair scheduler is considered as an attractive choice. The fairness is classified into short term fairness and long term fairness. It is known that the one-bit feedback fair scheduler has an ideal long term fairness property. However, the short term fairness properties of the scheduler have not been sufficiently explored yet. Since packet level performances of individual MSs (Mobile Stations) are strongly affected by short term fairness, it is also important to examine the short term fairness of the schedulers. In this paper, we focus on the short term fairness of the one-bit feedback fair scheduler. As a short term fairness index, we consider the Statistical Time-access fairness index (STAFI). We then develop two numerical methods to understand the transient properties of the STAFI of the one-bit feedback fair scheduler. The first method calculates the exact value of the STAFI by using the inverse discrete FFT method. The second method estimates the asymptotic decay rate of the STAFI by using the theory of large deviations.
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Effect of initial states on Statistical Time-access fairness index of one-bit feedback fair scheduler
2011 Third International Workshop on Cross Layer Design, 2011Co-Authors: Fumio IshizakiAbstract:Much attention has been paid to schedulers exploiting multiuser diversity in wireless networks, because such schedulers can increase the information theoretic capacity. It is, however, known that there exists a tradeoff between capacity and fairness achieved by schedulers exploiting multiuser diversity. Due to its good balance between capacity and fairness, the one-bit feedback fair scheduler is considered as an attractive choice. In this paper, we consider the one-bit feedback fair scheduler and investigate its short term fairness. In particular, we consider the Statistical Time-access fairness index (STAFI) as a short term fairness index, and we study the impact of the initial states of mobile stations (MSs) on the STAFI. Numerical results show that the effect of the initial states of MSs on the STAFI remains for a relatively long Time.
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Analysis of the Statistical Time-access fairness index under a scheduler exploiting multiuser diversity
2007 Wireless Telecommunications Symposium, 2007Co-Authors: Fumio Ishizaki, Chikara OhtaAbstract:Recent studies report that in wireless networks, schedulers exploiting multiuser diversity can substantially enhance the maximum throughput of the overall system. In this paper, we focus on a scheduler which maximizes the throughput of the overall system by exploiting multiuser diversity and is combined with adaptive modulation and coding. We numerically study the Statistical Time-access fairness of the scheduler. The transient Statistical fairness properties of schedulers exploiting multiuser diversity have not been sufficiently explored yet, although they are important in practice. We develop two numerical methods to understand the transient Statistical fairness properties of the scheduler. Numerical results exhibit that under the scheduler, serious unfairness between users can remain for a relatively long period even if the users have the same average SNR.
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WTS - Analysis of the Statistical Time-access fairness index under a scheduler exploiting multiuser diversity
2007 Wireless Telecommunications Symposium, 2007Co-Authors: Fumio Ishizaki, Chikara OhtaAbstract:Recent studies report that in wireless networks, schedulers exploiting multiuser diversity can substantially enhance the maximum throughput of the overall system. In this paper, we focus on a scheduler which maximizes the throughput of the overall system by exploiting multiuser diversity and is combined with adaptive modulation and coding. We numerically study the Statistical Time-access fairness of the scheduler. The transient Statistical fairness properties of schedulers exploiting multiuser diversity have not been sufficiently explored yet, although they are important in practice. We develop two numerical methods to understand the transient Statistical fairness properties of the scheduler. Numerical results exhibit that under the scheduler, serious unfairness between users can remain for a relatively long period even if the users have the same average SNR.
John S. Sakellariou - One of the best experts on this subject based on the ideXlab platform.
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Statistical Time Series Methods for Structural Health Monitoring ∗ (Encyclopedia of Structural Health Monitoring, John Wiley & Sons: Contribution ID shm044)
2020Co-Authors: Spilios D. Fassois, John S. SakellariouAbstract:Statistical Time series methods for structural health monitoring utilize random e xcitation and/or vibration response signals, Statistical model building, and Statistical decision making fo r inferring the health state of a structure. This includes fault detection, identification (localization), as well as ma gnitude estimation. The principles and operation of methods that utilize the Time or frequency domains on a periodic inspection basis are explained, and the methods are classified into various categories under the general non‐parametric and parametric classes. Representative methods from each category are outlined and their use is illustrated on a laboratory structure.
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Vibration-response-only Statistical Time series structural health monitoring methods: A comprehensive assessment via a scale jacket structure:
Structural Health Monitoring-an International Journal, 2019Co-Authors: Nikos A Spanos, John S. Sakellariou, Spilios D. FassoisAbstract:Random-vibration-based Statistical Time series structural health monitoring methods utilize small-scale, compact, and data-based, Time series stochastic representations of the structural dynamics f...
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vibration based damage detection for a population of nominally identical structures unsupervised multiple model mm Statistical Time series type methods
Mechanical Systems and Signal Processing, 2018Co-Authors: Kyriakos J Vamvoudakisstefanou, John S. Sakellariou, S D FassoisAbstract:Abstract The problem of vibration-based damage detection for a population of nominally identical structures is considered via unsupervised Statistical Time series type methods. For this purpose a population sample comprising 31 nominally identical composite beams with significant beam-to-beam variability in the dynamics is employed, with impact-induced damage at various positions and two distinct energy levels. Two Multiple Model, MM, based Statistical Time series type methods are postulated, assessed, and compared with two ‘conventional’ methods. The assessment is based on a comprehensive and systematic procedure, making use of thousands of test cases via a ‘rotation’ procedure, with the results presented in the form of Receiver Operating Characteristic, ROC, curves. These indicate that ‘conventional’ methods are mostly ineffective, especially with low impact energy damages. On the other hand, the postulated Multiple Model parameter based methods achieve significantly improved performance, characterized as very good and providing overall correct damage detection rates approaching 100 % for false alarm rates at or above 5 % .
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OUTPUT-ONLY Statistical Time SERIES METHODS FOR STRUCTURAL HEALTH MONITORING: A COMPARATIVE STUDY
2014Co-Authors: Kyriakos J. Vamvoudakis-stefanou, John S. Sakellariou, Spilios D. FassoisAbstract:A comparative assessment of six well known Output-Only Statistical Time Series Methods (OO-STSMs) for Structural Health Monitoring (SHM) is presented via damage detection and identification in a GARTEUR type aircraft skeleton structure. A concise overview of the methods highlighting their principles is presented and their effectiveness for damage detection and identification is assessed via numerous experiments and various damage cases on the skeleton structure. What is more, issues such as the methods effectiveness based on local or remote vibration sensors as well as their computational complexity and ease of use are also investigated.
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Encyclopedia of Structural Health Monitoring - Statistical Time Series Methods for SHM
Encyclopedia of Structural Health Monitoring, 2008Co-Authors: Spilios D. Fassois, John S. SakellariouAbstract:Statistical Time series methods for structural health monitoring utilize random excitation and/or vibration response signals, Statistical model building, and Statistical decision making for inferring the health state of a structure. This includes fault detection, identification (localization), as well as magnitude estimation. The principles and operation of methods that utilize the Time or frequency domains on a periodic inspection basis are explained, and the methods are classified into various categories under the general nonparametric and parametric classes. Representative methods from each category are outlined and their use is illustrated on a laboratory structure. Keywords: Statistical Time series methods; Statistical decision making; vibration-based methods; fault detection; fault identification; fault estimation; periodic inspection; Time-domain methods; frequency-domain methods