Damage Detection of Composite Beams via Variational Mode Decomposition of Shear-Slip Data

Sadeghi, Faraz; Mousavi, Mohsen; Zhu, Xinqun; Rashidi, Maria; Samali, Bijan; Gandomi, Amir H.

doi:10.1061/jsendh.steng-11309

Cited by 7 publications

(6 citation statements)

References 42 publications

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“…By testing their method on a numerical beam, they showed that VMD successfully localized damage when EMD could not. Similar conclusions were obtained by Sadeghi et al [ 60 ], who compared the use of VMD to EMD for localizing shear connectors damage in composite beams based on shear slip data. The change in energy in the second mode center frequencies is used as a damage indicator.…”

Section: Conventional Feature Extraction Techniquessupporting

confidence: 86%

Unsupervised Learning Methods for Data-Driven Vibration-Based Structural Health Monitoring: A Review

Eltouny

Gomaa

Liang

2023

Sensors

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Structural damage detection using unsupervised learning methods has been a trending topic in the structural health monitoring (SHM) research community during the past decades. In the context of SHM, unsupervised learning methods rely only on data acquired from intact structures for training the statistical models. Consequently, they are often seen as more practical than their supervised counterpart in implementing an early-warning damage detection system in civil structures. In this article, we review publications on data-driven structural health monitoring from the last decade that relies on unsupervised learning methods with a focus on real-world application and practicality. Novelty detection using vibration data is by far the most common approach for unsupervised learning SHM and is, therefore, given more attention in this article. Following a brief introduction, we present the state-of-the-art studies in unsupervised-learning SHM, categorized by the types of used machine-learning methods. We then examine the benchmarks that are commonly used to validate unsupervised-learning SHM methods. We also discuss the main challenges and limitations in the existing literature that make it difficult to translate SHM methods from research to practical applications. Accordingly, we outline the current knowledge gaps and provide recommendations for future directions to assist researchers in developing more reliable SHM methods.

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Section: Conventional Feature Extraction Techniquessupporting

confidence: 86%

Unsupervised Learning Methods for Data-Driven Vibration-Based Structural Health Monitoring: A Review

Eltouny

Gomaa

Liang

2023

Sensors

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“…can be acquired by equation (17). With the idea of a central limit theorem, the confidence interval lower limit for energy entropy distributions is determined as the state boundary:…”

Section: State Boundary Of Energy Entropy For Abnormality Detectionmentioning

confidence: 99%

“…To solve this problem, VMD is helpful to decompose the vibration signal into several mode components to obviate the occurrence of the modemixing phenomenon, and also acts as an adaptive signal analysis method [15]. The superiority of VMD has been effectively verified, and some successful applications can be found in [16,17].…”

Section: Introductionmentioning

confidence: 99%

A working-condition-robust method for two-stage health measurement of rolling bearings based on energy entropy distribution and dynamic adversarial transfer network

Jiang

Xue

Zhang

et al. 2023

Meas. Sci. Technol.

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Accurate and robust health measurement for rolling bearings under variable working conditions has great significance to guarantee the safe and stable operation of rotating machinery. In this paper, a two-stage and working condition-robust health measurement method is proposed, systematically blending energy entropy theory, deep learning approach and transfer learning technology. In the first stage, a state boundary of energy entropy is systematically deduced based on the adaptive variational mode decomposition (VMD) – improved fruit fly optimization algorithm (IFOA) and statistical analysis principle to detect the abnormal states of bearings, where the IFOA is developed for searching the optimal parameters of VMD with high efficiency. In the second stage, if fault exists, a hybrid robust auto-encoder (HRAE) adopting the multi-layer and deep structure is constructed to strengthen the features extraction capacity and automatically capture valuable and robust fault features from original samples. Considering the insufficient labeled samples and significant data distribution discrepancy, a novel dynamic adversarial transfer network (DATN) is designed to extract the transferable and domain-invariant features between source and target datasets and achieve accurate fault identification. Specifically, a dynamic adversarial coefficient based on Wasserstein distance is provided in DATN to quantitatively evaluate the relative importance of marginal and conditional distributions. Extensive experiments on two rolling bearing datasets validate the more superior performance of the proposed method compared with other state-of-the-art identification models and transfer learning approaches.

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“…How to find an effective algorithm to better evaluate the modal parameters of the bridge from the noisy signal responses has consistently attracted the interest of researchers. Some time-frequency domain analysis methods are employed to decompose signal responses into several intrinsic mode functions (IMFs) and thus separate noise components, like empirical wavelet transform (EWT) [14,15], empirical mode decomposition (EMD) [16][17][18], ensemble empirical mode decomposition (EEMD) [19][20][21], complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) [22][23][24], variational mode decomposition (VMD) [25][26][27][28]. Tao et al [29] proposed the EWT method to reduce the noise in the GNSS coordinate system and demonstrated that the signal after EWT denoising was close to the original signal.…”

Section: Introductionmentioning

confidence: 99%

Modal frequencies evaluation of a damaged bridge using RCVMD algorithm based on sensor dynamic responses

Xiong

Shang

Liu

et al. 2023

Meas. Sci. Technol.

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This paper aims to employ high-rate global navigation satellite system real-time kinematic (GNSS-RTK) and triaxial accelerometer sensors to evaluate the modal frequencies of a damaged long-span bridge based on the proposed modified variational mode decomposition (VMD) algorithm. Firstly, in the modified VMD, the relative root mean square error (RRMSE) was presented to optimize the decomposition parameter K, the correlation criterion was introduced to select the suitable intrinsic mode function (IMF) components. The above procedure is called the RCVMD algorithm. Subsequently, the performance comparison evaluation experiments proved that the RCVMD algorithm was more advantageous in the decomposition and reconstruction of signals, had a better noise reduction effect in the signal with different noise levels, and was suitable to process the actual measurement response. Finally, in the field experiment, the modal frequencies of the damaged bridge were correctly detected from the GNSS-RTK displacement and acceleration responses according to the RCVMD algorithm, and the accelerometer picked up the high-order structural frequencies, demonstrating the reliability of the proposed method. The frequency characteristics of the damaged bridge are different from the normal state. The first and third-order frequencies increase, which is especially obvious in the first-order frequency, yet the second-order frequency decreases. The measured results have practical applications and can provide data support for the maintenance of the damaged bridge.

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Damage Detection of Composite Beams via Variational Mode Decomposition of Shear-Slip Data

Cited by 7 publications

References 42 publications

Unsupervised Learning Methods for Data-Driven Vibration-Based Structural Health Monitoring: A Review

Unsupervised Learning Methods for Data-Driven Vibration-Based Structural Health Monitoring: A Review

A working-condition-robust method for two-stage health measurement of rolling bearings based on energy entropy distribution and dynamic adversarial transfer network

Modal frequencies evaluation of a damaged bridge using RCVMD algorithm based on sensor dynamic responses

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