Statistical Time Series Methods for Vibration Based Structural Health Monitoring

Fassois, Spilios D.; Kopsaftopoulos, Fotis

doi:10.1007/978-3-7091-1390-5_4

Cited by 65 publications

(161 citation statements)

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“…On the other hand, the method's performance depends on the quality of the identified partial system functional models that represent the railway vehicle under different types of faults in a continuum range of fault magnitudes, necessitating caution and expertise in the identification procedure. Furthermore, from its nature as a data-based method [25,26], it needs data from the healthy and faulty vehicle for its training in a baseline phase. This is the main practical difficulty of most data-based methods as data from the faulty system is not often available.…”

Section: Introductionmentioning

confidence: 99%

Vibration based fault diagnosis for railway vehicle suspensions via a functional model based method: A feasibility study

2015

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The design of a vibration based fault detection and isolation (FDI) unit that can tackle the combined problem of fault detection, isolation (or identification) and magnitude estimation (collectively known as fault diagnosis), in railway vehicle suspensions is presented. The unit is initially "trained" in a baseline phase based on data obtained from a simplified physics-based model of a railway vehicle suspension. Fault diagnosis is subsequently achieved in an inspection phase through a single, properly preselected, pair of vibration signals acquired from the vehicle, and a recently introduced data-based method, referred to as the functional model based method (FMBM), without resorting to the physics-based model of the baseline phase. The method's cornerstone is the novel class of stochastic ARX-type models capable of accurately representing a system in a faulty state for its continuum of fault magnitudes. Fault diagnosis feasibility in a railway vehicle suspension is demonstrated via Monte Carlo simulations using different types and magnitudes of faults in the physics-based model and generating vibration signals corresponding to the healthy and faulty suspension. Two vibration signals are used by the diagnosis unit: the track velocity profile and the vehicle body acceleration above the trailing airspring. Fault diagnosis based on the FMBM is effective in a compact and unified statistical framework accounting for experimental and modelling uncertainty through appropriate interval estimates and hypothesis testing procedures. The unit is shown to exhibit high sensitivity and accurate estimation of even very small fault magnitudes, to detect and isolate unknown faults for which it has not been trained, and to be robust to high measurement noise, car body mass variations, and varying track irregularity.

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Section: Introductionmentioning

confidence: 99%

Vibration based fault diagnosis for railway vehicle suspensions via a functional model based method: A feasibility study

2015

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“…Over the past two decades, the data-driven time series methods utilizing statistical signal processing techniques have formed an important and rapidly evolving class of vibration based methods [16,36]. These methods rely on the well-established statistical concepts instead of human expertise to extract features that change with the onset of damage [37], which eliminates possible individual biases and requires very few assumptions regarding the physical structure.…”

Section: Data-based Statistical Methodsmentioning

confidence: 99%

The State-of-the-Art on Framework of Vibration-Based Structural Damage Identification for Decision Making

2017

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Abstract:Research on detecting structural damage at the earliest possible stage has been an interesting topic for decades. Among them, the vibration-based damage detection method as a global technique is especially pervasive. The present study reviewed the state-of-the-art on the framework of vibration-based damage identification in different levels including the prediction of the remaining useful life of structures and the decision making for proper actions. This framework consists of several major parts including the detection of damage occurrence using response-based methods, building reasonable structural models, selecting damage parameters and constructing objective functions with sensitivity analysis, adopting optimization techniques to solve the problem, predicting the remaining useful life of structures, and making decisions for the next actions. For each part, the commonly used methods were reviewed and the merits and drawbacks were summarized to give recommendations. This framework is aimed to guide the researchers and engineers to implement step by step the structure damage identification using vibration measurements. Finally, the future research work in this field is recommended.

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“…In this last context, classical hypothesis test is used to compare extracted statistical quantities from statistical time series models like mean, normalized autocovariance function, cross covariance function, power spectral density, cross spectral density, frequency response function, squared coherence, residual variance, likelihood function, residual sequences, among others [7]. A hypothesis testing technique called a sequential probability ratio test (SPRT) has been combined with time series analysis and neural networks for damage classification in [18].…”

Section: Introductionmentioning

confidence: 99%

“…As said before, the use of hypothesis testing is not new in this field. The novelty of the previous work [15] is based on (i) the nature of the data used in the test since we are using scores instead of the measured response of the structure [7] or the coefficients of an AutoRegressive model [21]; (ii) the number of data used since our test is based on two random samples instead of two characteristic quantities [6]. The proposed development starts obtaining the baseline PCA model and the subsequent projections using the healthy structure.…”

Section: Introductionmentioning

confidence: 99%

Detection of structural changes through principal component analysis and multivariate statistical inference

Pozo

Arruga

Mujica

et al. 2016

Structural Health Monitoring

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Statistical Time Series Methods for Vibration Based Structural Health Monitoring

Cited by 65 publications

References 64 publications

Vibration based fault diagnosis for railway vehicle suspensions via a functional model based method: A feasibility study

Vibration based fault diagnosis for railway vehicle suspensions via a functional model based method: A feasibility study

The State-of-the-Art on Framework of Vibration-Based Structural Damage Identification for Decision Making

Detection of structural changes through principal component analysis and multivariate statistical inference

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