Scalar and Vector Time Series Methods for Vibration Based Damage Diagnosis in an Aircraft Scale Skeleton Structure

Kopsaftopoulos, Fotis; Magripis, Spiros G.; Amplianitis, Aris; Fassois, Spilios D.

doi:10.1115/esda2010-24275

Cited by 18 publications

(46 citation statements)

References 7 publications

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“…A related assessment of many methods may be found in a recent work by the authors [12]. The present study is an extension that also presents results with an additional, statistically optimal, method based on the Sequential Probability Ratio Test (SPRT) [13,14].…”

Section: Introductionmentioning

confidence: 93%

Statistical time series methods for damage diagnosis in a scale aircraft skeleton structure: loosened bolts damage scenarios

Kopsaftopoulos

Fassois

2011

J. Phys.: Conf. Ser.

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Section: Introductionmentioning

confidence: 93%

Statistical time series methods for damage diagnosis in a scale aircraft skeleton structure: loosened bolts damage scenarios

Kopsaftopoulos

Fassois

2011

J. Phys.: Conf. Ser.

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“…An additional assessment, employing five distinct damages with each corresponding to complete loosening of one or two bolts on a lab-scale lightweight truss structure, is presented in Kopsaftopoulos and Fassois. 18 Multi-sensor (vector) methods, along with single-sensor (scalar) counterparts, are assessed in Kopsaftopoulos and Fassois 19 with six distinct damages, each corresponding to complete loosening of one to four bolts on the lab-scale aircraft skeleton structure. An additional, interesting, aspect considered is the sensitivity of the diagnostic performance upon sensor proximity to damage, which is found to be insignificant – a result offering further attestation to the global nature of the methods, which is essential for operation with a minimal number of sensors.…”

Section: Introductionmentioning

confidence: 99%

Vibration-response-only statistical time series structural health monitoring methods: A comprehensive assessment via a scale jacket structure

Spanos

Sakellariou

Fassois

2019

Structural Health Monitoring

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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 for damage diagnosis. In this study, a comprehensive and critical assessment of the diagnostic performance of five prominent response-only methods is presented based on incipient, ‘minor’ to ‘mild’, damages on a lab-scale wind turbine jacket structure. Statistically reliable damage detection and identification results are obtained via a ‘rotation’ procedure resulting into thousands of test cases, with the performance analysed in terms of receiver operating characteristic curves and confusion matrices. The results indicate not only challenging of the methods’ capabilities, but also the achievement of good to excellent performance for the ‘minor’ to ‘mild’ damages, respectively, with the model parameter–based method offering the best performance. In addition, the use of a vibration signal measured via a laser vibrometer leads to slightly improved detection performance over that obtained via a classical accelerometer.

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“…8,9,[17][18][19][20][21][22] For an extended overview of the main statistical time series methods for SHM, the interested reader is referred to Fassois and Sakellariou,5,6 while experimental assessments of various scalar and vector methods are provided in Kopsaftopoulos and Fassois. 9,23 The vast majority of statistical time series SHM methods is based on fixed sample size (FSS) hypothesis testing procedures which are used in the statistical decision making. FSS hypothesis testing employs a constant amount of observations, which is determined a priori of the experimental data acquisition.…”

Section: Introductionmentioning

confidence: 99%

A vibration model residual-based sequential probability ratio test framework for structural health monitoring

Kopsaftopoulos

Fassois

2015

Structural Health Monitoring

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The goal of this study is the introduction and experimental assessment of a sequential probability ratio test framework for vibration-based structural health monitoring. This framework is based on a combination of binary and multihypothesis versions of the statistically optimal sequential probability ratio test and employs the residual sequences obtained through a single stochastic time series model of the healthy structure. The full list of properties and capabilities of the sequential probability ratio test is for the first time presented and explored in the context of vibration-based damage diagnosis. The approach postulated in this framework is shown to achieve early and robust damage detection, identification (classification), and quantification based on predetermined sampling plans, which are both analytically and experimentally compared and assessed. The framework's performance is determined a priori via the use of the analytical expressions of the operating characteristic and average sample number functions in combination with baseline data records. It is shown to require, on average, a minimal number of signal samples in order to reach a decision compared to fixed sample size 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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Scalar and Vector Time Series Methods for Vibration Based Damage Diagnosis in an Aircraft Scale Skeleton Structure

Cited by 18 publications

References 7 publications

Statistical time series methods for damage diagnosis in a scale aircraft skeleton structure: loosened bolts damage scenarios

Statistical time series methods for damage diagnosis in a scale aircraft skeleton structure: loosened bolts damage scenarios

Vibration-response-only statistical time series structural health monitoring methods: A comprehensive assessment via a scale jacket structure

A vibration model residual-based sequential probability ratio test framework for structural health monitoring

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