Extraction of bridge frequencies from the dynamic response of a passing vehicle enhanced by the EMD technique

Yang, Y.B.; Chang, Kai-Chun

doi:10.1016/j.jsv.2008.11.028

Cited by 193 publications

(62 citation statements)

References 7 publications

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“…The feasibility of extracting bridge dynamic parameters, such as damping and frequency, from the vehicle response has been verified theoretically (Yang et al 2004b, McGetrick et al 2009 and it has also been tested in field trials (Lin and Yang 2005, Oshima et al 2008, Yang and Chang 2009a. Parametric studies have been carried out which indicate favourable conditions for its implementation (Yang and Chang 2009b).…”

Section: Implementation Of a Drive-by Inspection Algorithmmentioning

confidence: 95%

A drive-by inspection system via vehicle moving force identification

O’Brien

McGetrick²,

González

2014

Smart Structures and Systems

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Publication informationSmart Structures and Systems, 13 (5) Abstract. This paper presents a novel method to carry out monitoring of transport infrastructure such as pavements and bridges through the analysis of vehicle accelerations. An algorithm is developed for the identification of dynamic vehicle-bridge interaction forces using the vehicle response. Moving force identification theory is applied to a vehicle model in order to identify these dynamic forces between the vehicle and the road and/or bridge. A coupled half-car vehicle-bridge interaction model is used in theoretical simulations to test the effectiveness of the approach in identifying the forces. The potential of the method to identify the global bending stiffness of the bridge and to predict the pavement roughness is presented. The method is tested for a range of bridge spans using theoretical simulations and the influences of road roughness and signal noise on the accuracy of the results are investigated.

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Section: Implementation Of a Drive-by Inspection Algorithmmentioning

confidence: 95%

A drive-by inspection system via vehicle moving force identification

O’Brien

McGetrick²,

González

2014

Smart Structures and Systems

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“…After EMD was developed, it has been widely studied in various domains, such as process control [38], voice recognition [39], and system identification [40]. The decomposition result of a simple sample signal is shown in Figure 3.…”

Section: Empirical Mode Decompositionmentioning

confidence: 99%

Fault Diagnosis for Rotating Machinery Based on Convolutional Neural Network and Empirical Mode Decomposition

Xie

Zhang

2017

Shock and Vibration

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The analysis of vibration signals has been a very important technique for fault diagnosis and health management of rotating machinery. Classic fault diagnosis methods are mainly based on traditional signal features such as mean value, standard derivation, and kurtosis. Signals still contain abundant information which we did not fully take advantage of. In this paper, a new approach is proposed for rotating machinery fault diagnosis with feature extraction algorithm based on empirical mode decomposition (EMD) and convolutional neural network (CNN) techniques. The fundamental purpose of our newly proposed approach is to extract distinguishing features. Frequency spectrum of the signal obtained through fast Fourier transform process is trained in a designed CNN structure to extract compressed features with spatial information. To solve the nonstationary characteristic, we also apply EMD technique to the original vibration signals. EMD energy entropy is calculated using the first few intrinsic mode functions (IMFs) which contain more energy. With features extracted from both methods combined, classification models are trained for diagnosis. We carried out experiments with vibration data of 52 different categories under different machine conditions to test the validity of the approach, and the results indicate it is more accurate and reliable than previous approaches.

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“…Identification of the bridge natural frequencies using indirect measurements has been extensively investigated by many researchers [4][5][6]. Yang and Chen [7] propose a modified version of the Stochastic Subspace Identification (SSI) method for estimating the bridge natural frequencies from the responses measured on a vehicle.…”

Section: Introductionmentioning

confidence: 99%