EEMD-MUSIC-Based Analysis for Natural Frequencies Identification of Structures Using Artificial and Natural Excitations

Camarena-Martinez, David; Amezquita-Sanchez, Juan P.; Valtierra-Rodríguez, Martín; Romero-Troncoso, René de Jesús; Osornio-Ríos, Roque Alfredo; García-Pérez, Arturo

doi:10.1155/2014/587671

Cited by 17 publications

(5 citation statements)

References 28 publications

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“…In this way, EEMD populates the whole time-frequency space and IMFs are obtained from the ensemble mean of the corresponding IMFs. For a signal ( ), EEMD algorithm mainly includes the following steps [22].…”

Section: Eemd Methodmentioning

confidence: 99%

An Improved Method Based on CEEMD for Fault Diagnosis of Rolling Bearing

Wang

Tang

et al. 2014

Advances in Mechanical Engineering

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In order to improve the effectiveness for identifying rolling bearing faults at an early stage, the present paper proposed a method that combined the so-called complementary ensemble empirical mode decomposition (CEEMD) method with a correlation theory for fault diagnosis of rolling element bearing. The cross-correlation coefficient between the original signal and each intrinsic mode function (IMF) was calculated in order to reduce noise and select an effective IMF. Using the present method, a rolling bearing fault experiment with vibration signals measured by acceleration sensors was carried out, and bearing inner race and outer race defect at a varying rotating speed with different degrees of defect were analyzed. And the proposed method was compared with several algorithms of empirical mode decomposition (EMD) to verify its effectiveness. Experimental results showed that the proposed method was available for detecting the bearing faults and able to detect the fault at an early stage. It has higher computational efficiency and is capable of overcoming modal mixing and aliasing. Therefore, the proposed method is more suitable for rolling bearing diagnosis.

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Section: Eemd Methodmentioning

confidence: 99%

An Improved Method Based on CEEMD for Fault Diagnosis of Rolling Bearing

Wang

Tang

et al. 2014

Advances in Mechanical Engineering

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“…3 show that only two modes exist in the frequency domain below 400 kHz [14]: S 0 , fundamental symmetric mode (phase velocity: 5.3 km/ s) and A 0 , fundamental antisymmetric mode (phase velocity: 1.5 km/s).…”

Section: Experimental Arrangementmentioning

confidence: 96%

“…The method proposed in this work consists of the combination of the empirical mode decomposition associated with the Hilbert spectral analysis. Camarena-Martinez et al [14] illustrated the suitability of the HHT-based methodology to identify modal frequencies in structures ranging from free and ambient vibration signals produced by artificial and natural excitations. Yu et al [22] proposed a method for the fault diagnosis of roller bearings based on the HHT.…”

Section: Procedures To Identify the Location Of Damagementioning

confidence: 99%

Detection and localization of damage using empirical mode decomposition and multilevel support vector machine

2016

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Damage in the structure may raise a significant amount of maintenance cost and serious safety problems. Hence detection of the damage at its early stage is of prime importance. The main contribution pursued in this investigation is to propose a generic optimal methodology to improve the accuracy of positioning of the flaw in a structure. This novel approach involves a two-step process. The first step essentially aims at extracting the damagesensitive features from the received signal, and these extracted features are often termed the damage index or damage indices, serving as an indicator to know whether the damage is present or not. In particular, a multilevel SVM (support vector machine) plays a vital role in the distinction of faulty and healthy structures. Formerly, when a structure is unveiled as a damaged structure, in the subsequent step, the position of the damage is identified using Hilbert-Huang transform. The proposed algorithm has been evaluated in both simulation and experimental tests on a 6061 aluminum plate with dimensions 300 mm 9 300 mm 9 5 mm which accordingly yield considerable improvement in the accuracy of estimating the position of the flaw.

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“…However, VMD's decomposition number K lacks unifed theoretical guidance and needs to be determined artifcially [68]. Tey currently have numerous applications [69][70][71][72].…”

Section: Structural Control and Health Monitoringmentioning

confidence: 99%

Complex Background Removal Method for Video of an Inclined Cable Based on UAV Detection of Structural Vibration Frequencies

Li,

Cao,

et al. 2024

Structural Control and Health Monitoring

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The safety of inclined cables is fundamental to the integrity of cable-stayed bridges. The vibrational frequencies of these cables form the foundation for assessing the cable force. Traditional contact measurement methods necessitate the installation of sensors on each cable, incurring substantial costs. In scenarios where camera placement adjacent to an inclined cable is impractical, noncontact approaches such as video capture via unmanned aerial vehicles prove effective. However, unmanned aerial vehicle-captured videos present a challenge due to their complex background, impeding cable feature recognition. In our study, we initially utilized the Region Growing algorithm for background subtraction. To enhance this method, we integrated it with the unique structural characteristics of cables, leading to the creation of the RGv2 algorithm. This novel algorithm offers increased processing speed and improved accuracy. Furthermore, we combined our method with empirical mode decomposition for effective detection of cable frequency characteristics. We also implemented a hybrid method, combining the K-Means and line segment detector algorithms with empirical mode decomposition. Compared to deep learning techniques for background subtraction, our proposed method demonstrates superior computational efficiency and promising potential for measuring vibrational frequencies of inclined cables.

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EEMD-MUSIC-Based Analysis for Natural Frequencies Identification of Structures Using Artificial and Natural Excitations

Cited by 17 publications

References 28 publications

An Improved Method Based on CEEMD for Fault Diagnosis of Rolling Bearing

An Improved Method Based on CEEMD for Fault Diagnosis of Rolling Bearing

Detection and localization of damage using empirical mode decomposition and multilevel support vector machine

Complex Background Removal Method for Video of an Inclined Cable Based on UAV Detection of Structural Vibration Frequencies

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