Vibration based fault detection and identification in an aircraft skeleton structure via a stochastic functional model based method

Sakellariou, John S.; Fassois, Spilios D.

doi:10.1016/j.ymssp.2007.09.002

Cited by 47 publications

(81 citation statements)

References 22 publications

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“…The unit achieves fault diagnosis based, for the first time, on a recently introduced data-based method, the functional model based method (FMBM) [22], which operates in a unified and compact statistical framework that accounts for experimental and modelling uncertainty through few (even a single pair) vibration signals from a simplified railway vehicle model. Some early results were presented in a preliminary study [23]; several improvements on issues such as the unit's sensitivity to incipient faults and its robustness to experimental and operational uncertainties are currently implemented.…”

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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“…This idea is not revolutionary since several attempts to test damage detection algorithms with mass addition have been made before as in [12][13][14]. In addition, specifically added mass localisation was done in [15] with the help of a concentrated mass on a rectangular plate, while in [16] added masses were additionally assessed in terms of their weight in an attempt to perform damage severity on an aircraft skeleton wing.…”

Section: Introductionmentioning

confidence: 99%

The use of pseudo-faults for damage location in SHM: An experimental investigation on a Piper Tomahawk aircraft wing

Papatheou

Manson

Barthorpe

et al. 2014

Journal of Sound and Vibration

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The application of pattern recognition-based approaches in damage localisation and quantification will eventually require the use of some kind of supervised learning algorithm. The use, and most importantly, the success of such algorithms will depend critically on the availability of data from all possible damage states for training. It is perhaps well known that the availability of damage data through destructive means cannot generally be afforded in the case of high value engineering structures outside laboratory conditions. This paper presents the attempt to use added masses in order to identify features suitable for training supervised learning algorithms and then subsequently test the trained classifiers with damage data, with the ultimate purpose of damage localisation. In order to test the approach of adding masses, two separate cases of a dual-class classification problem, representing two distinct locations, and a three-class problem representing three distinct locations, are examined with the help of a full-scale aircraft wing. It was found that an excellent rate of correct classification could be achieved in both the dual-class and threeclass cases. However, it was also found that the rate of correct classification was sensitive to the choices made in training the supervised learning algorithm. The results for the dual-class problem demonstrated a comparatively high level of robustness to these choices with a substantially lower robustness found in the three-class case.

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“…They offer a number of advantages, including no requirement for physics based or finite element models, no requirement for complete modal models, effective treatment of uncertainties, and statistical decision making with specified performance characteristics [1,2]. These methods form an important and useful category within the broader family of vibration based methods for SHM [3,4].…”

Section: Introductionmentioning

confidence: 99%

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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Vibration based fault detection and identification in an aircraft skeleton structure via a stochastic functional model based method

Cited by 47 publications

References 22 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 use of pseudo-faults for damage location in SHM: An experimental investigation on a Piper Tomahawk aircraft wing

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

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