Damage localization using transmissibility functions: A critical review

Chesné, Simon; Deraemaeker, Arnaud

doi:10.1016/j.ymssp.2013.01.020

Cited by 138 publications

(94 citation statements)

References 30 publications

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“…Devriendt et al [74][75][76] used transmissibility to identify modal parameters from output-only transmissibility measurements and multivariable transmissibility measurements. Chesne and Deraemaeker [77] reviewed the state-of-the-art of using transmissibility functions for damage localization of mechanical systems. Kong et al [78] studied the feasibility of detecting bridge damage using transmissibility of the vehicle response in a vehicle-bridge coupled system.…”

Section: Frequency Domain 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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Section: Frequency Domain Methodsmentioning

confidence: 99%

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

2017

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“…Among others global SHM methods, we can highlight vibration based approaches [4,5]. These methods are categorized based on the type and nature of measured data.…”

Section: Damage-sensitive Features Generated Using Amplitude Disturbementioning

confidence: 99%

A probabilistic multi-class classifier for structural health monitoring

Mechbal

Uribe

Rébillat

2015

Mechanical Systems and Signal Processing

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is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. Abstract. In this paper, a probabilistic multi-class pattern recognition algorithm is developed for damage detection, localization, and quantification in smart mechanical structures. As these structures can face damages of different severities located at various positions, multi-class classifiers are naturally needed in that context. Furthermore, because of the lack of available data in the damaged state and of environmental effects, the experimentally obtained damage sensitive features may differ from those learned offline by the classifier. A multiclass classifier that provides probabilities associated with each damage severity and location instead of a binary decision is thus greatly desirable in that context. To tackle this issue, we propose an original support vector machine (SVM) multi-class clustering algorithm that is based on a probabilistic decision tree (PDT) and that produces a posteriori probabilities associated with damage existence, location, and severity. Furthermore, the PDT is here built by iteratively subdividing the surface of the structure and thus takes into account the actual structure geometry. The proposed algorithm is very appealing as it combines both the computational efficiency of tree architectures and the classification accuracy of SVMs. The effectiveness of this algorithm is illustrated experimentally on a composite plate instrumented with piezoelectric elements on which damages are simulated using added masses. Damage sensitive features are computed using an active approach based on the permanent emission of non-resonant Lamb waves into the structure and on the recognition of amplitude disturbed diffraction patterns. On the basis of these damage-sensitive features, the proposed multi-class probabilistic classifier generates decisions that are in excellent agreement with the actual severities and locations of the simulated damages.

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“…The proposed approach was validated on data from a physics-based numerical model as well as experimental data from a three-story aluminum frame structure. Chesné and Deraemaeker [9] reviewed the stateof-the-art using TF for damage detection and localization and discussed the extension of these results to more general dispersive systems such as beams or plates. Zhu et al [3] proposed a decentralized damage detection approach using TF, which was ideal for using limited number of sensors upon a large-scale structure.…”

Section: Introductionmentioning

confidence: 99%

Structural Damage Identification Based on the Transmissibility Function and Support Vector Machine

Diao

Men

Sun

et al. 2018

Shock and Vibration

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A novel damage identification method based on transmissibility function and support vector machine is proposed and outlined in this paper. Basically, the transmissibility function is calculated with the acceleration responses from damaged structure. Then two damage features, namely, wavelet packet energy vector and the low order principal components, are constructed by analyzing the amplitude of the transmissibility function with wavelet packet decomposition and principal component analysis separately. Finally, the classification algorithm and regression algorithm of support vector machine are employed to identify the damage location and damage severity respectively. The numerical simulation and shaking table model test of an offshore platform under white noise excitation are conducted to verify the proposed damage identification method. The results show that the proposed method does not need the information of excitation and the data from undamaged structure, needs only small size samples, and has certain antinoise ability. The detection accuracy of the proposed method with damage feature constructed by principal component analysis is superior to that constructed by wavelet packet decomposition.

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Damage localization using transmissibility functions: A critical review

Abstract: Abstract:This

Cited by 138 publications

References 30 publications

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

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

A probabilistic multi-class classifier for structural health monitoring

Structural Damage Identification Based on the Transmissibility Function and Support Vector Machine

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