Structural Damage Detection by Integrating Independent Component Analysis and Support Vector Machine

Song, Huazhu; Zhong, Luo; Han, Bo

doi:10.1007/11527503_79

Cited by 12 publications

(7 citation statements)

References 6 publications

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“…In this system, the excitation and vibration of the bridge are the input and output of the system, respectively. They seem to be related, but actually they are statistically independent, as verified by many research results . We calculate the power value to check which signal is the most powerful and can represent the bridge's vibration.…”

Section: Bridge Damage Diagnosis Algorithmmentioning

confidence: 70%

“…Most of the existing methods can be classified into two groups: model‐based and feature‐based. Model‐based methods are model updating procedures in which the structure's physical parameters, such as modal properties (e.g., mode shapes, natural frequencies and damping ratios), are calibrated and updated using measured data . A fundamental difficulty with these methods is that physical parameters obtained from the updating procedure may be unrelated to the actual damage scenarios (location and severity), although they can be consistent with the measured modal data.…”

Section: Introductionmentioning

confidence: 99%

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A novel bridge structure damage diagnosis algorithm based on post‐nonlinear ICA and statistical pattern recognition

Xiao

Lei

Ogai

2015

IEEJ Transactions Elec Engng

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Monitoring the health of bridges and diagnosing the damage is vital for government and related institutions in Japan because of frequent earthquakes and the oceanic climate. This paper develops a bridge structure health monitoring system (BSHM), which includes data acquisition and analysis. A two‐stage structure damage detection algorithm based on post‐nonlinear independent component analysis (ICA) and statistical pattern recognition is proposed to analyze the acquired data and evaluate the health of bridges. First, an improved post‐nonlinear ICA algorithm is proposed for denoising, and a data‐sample matching based data normalization scheme to reduce the effect of varying environmental and operational condition. Thereafter, fast Fourier transform (FFT) is used to detect the damage. Based on the first stage, a statistical pattern recognition damage detection algorithm, including a new damage sensitive index DSPR, is proposed to determine the severity and location(s) of damage. In addition to the algorithm, this paper presents several simulations and experiments, including a detection experiment that applies artificial damage to a real bridge to show that our design choices are indeed effective. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Section: Bridge Damage Diagnosis Algorithmmentioning

confidence: 70%

Section: Introductionmentioning

confidence: 99%

A novel bridge structure damage diagnosis algorithm based on post‐nonlinear ICA and statistical pattern recognition

Xiao

Lei

Ogai

2015

IEEJ Transactions Elec Engng

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“…For example, Zang et al [27] evaluated a combination of ICA extraction and artificial neuronal networks (ANN) for damage detection. Song et al [28] presented an approach that integrated the calculation of the independent components (ICs) to extract features with the information of the damage level and type, and a support vector machine classifier to detect damage. Senguler et al [29] used ICA for feature extraction and wavelet packet decomposition for monitoring the incipient bearing damage in electrical motors.…”

Section: Introductionmentioning

confidence: 99%

Investigation of an expert health monitoring system for aeronautical structures based on pattern recognition and acousto-ultrasonics

Burgos

Torres-Arredondo²

2015

Smart Mater. Struct.

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Aeronautical structures are subjected to damage during their service raising the necessity for periodic inspection and maintenance of their components so that structural integrity and safe operation can be guaranteed. Cost reduction related to minimizing the out-of-service time of the aircraft, together with the advantages offered by real-time and safe-life service monitoring, have led to a boom in the design of inexpensive and structurally integrated transducer networks comprising actuators, sensors, signal processing units and controllers. These kinds of automated systems are normally referred to as smart structures and offer a multitude of new solutions to engineering problems and multi-functional capabilities. It is thus expected that structural health monitoring (SHM) systems will become one of the leading technologies for assessing and assuring the structural integrity of future aircraft. This study is devoted to the development and experimental investigation of an SHM methodology for the detection of damage in real scale complex aeronautical structures. The work focuses on each aspect of the SHM system and highlights the potentialities of the health monitoring technique based on acousto-ultrasonics and data-driven modelling within the concepts of sensor data fusion, feature extraction and pattern recognition. The methodology is experimentally demonstrated on an aircraft skin panel and fuselage panel for which several damage scenarios are analysed. The detection performance in both structures is quantified and presented.

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“…Based on the optimization ability of the intelligent algorithms, these methods can simulate measured parameters and then determine the finite element (FE) model that can reflect the actual structure condition, which enables these methods to identify the locations and degrees of damage accurately. Damage detection methods have been reported to use algorithms, including the artificial neural network (ANN) (Wu et al, 1992;Parka et al, 2009), support vector machine (SVM) (Song et al, 2006;He and Yan, 2007), and genetic algorithm (GA) (Mares and Surace, 1996;He and Hwang, 2006;Gomes and Silva, 2008;Vakil-Baghmisheh et al, 2008;Meruane and Heylen, 2011). However, the ANN and SVM require neutral training, in which a large amount of data is demanded.…”

Section: Introductionmentioning

confidence: 99%

Intelligent damage identification method for large structures based on strain modal parameters

Lian

2013

Journal of Vibration and Control

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Early damage detection not only improves the safety and reliability of structures but also reduces maintenance cost. However, damage detection is difficult to implement in large structures under ambient excitation because of the limitation of sensors, the uncertainty of ambient excitation, and the global properties of modal frequencies and displacement modes. This paper proposes a new damage detection method that employs the real encoding multi-swarm particle swarm optimization algorithm and fitness functions evolved from strain modes to find the optimal match between measured and simulated modal parameters and to determine the actual condition of structures. The proposed method requires low-frequency modes and incomplete modes and does not require mass normalization of parameters, thus making the method suitable for nondestructive dynamic damage detection of large structures under ambient excitation. Taking a concrete guide wall structure as an example, this paper studied the global searching performance and the sensitivity of the proposed method. The efficiency of the proposed method was analyzed by using different noise levels and sensor numbers. Results show that the proposed method is effective and can be applied in many types of large structures.

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Structural Damage Detection by Integrating Independent Component Analysis and Support Vector Machine

Cited by 12 publications

References 6 publications

A novel bridge structure damage diagnosis algorithm based on post‐nonlinear ICA and statistical pattern recognition

A novel bridge structure damage diagnosis algorithm based on post‐nonlinear ICA and statistical pattern recognition

Investigation of an expert health monitoring system for aeronautical structures based on pattern recognition and acousto-ultrasonics

Intelligent damage identification method for large structures based on strain modal parameters

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