Fuzzy Cluster Analysis Methods Applied to Impedance Based Structural Health Monitoring for Damage Classification

Palomino, Lizeth Vargas; Steffen, Valder; Finzi, Roberto Mendes

doi:10.1007/978-1-4614-2419-2_18

Cited by 4 publications

(4 citation statements)

References 17 publications

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“…The experimental studies showed that the approach is able to identify systematical debonding in FRP-strengthened RC beams. Palomino et al (2012) applied the fuzzy cluster analysis for EMI technique based damage identification and classification. Loosen rivet and a small hole with 1 mm diameter were introduced to the beam structure as the incipient damages.…”

Section: Data-driven Based Emi Techniquesmentioning

confidence: 99%

“…Considerable efforts have been made to study and improve the EMI technique to overcome such limitation. On one hand, combinations of using statistical damage indicators of impedance responses and other pattern recognition algorithms, such as Artificial Neural Networks (ANNs) (He et al, 2014; Lopes et al, 2000; Selva et al, 2013), Probabilistic Neural Network (PNN) (Na and Lee, 2013), clustering analysis (Langone et al, 2017; Palomino et al, 2012), etc., have been investigated to improve the performance of EMI based damage identification methods. On the other hand, studies on using EMI based damage detection methods and model updating have been carried out to overcome the limitation of classifying phenomenological characterizations with statistical indicators (Cao et al, 2018; Fan et al, 2018b; Shuai et al, 2017; Wang and Tang, 2009), since the aforementioned neural network based EMI methods still cannot relate the changes of impedance responses to changes in structural physical properties, such as stiffness, mass or damping [27].…”

Section: Introductionmentioning

confidence: 99%

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Review of piezoelectric impedance based structural health monitoring: Physics-based and data-driven methods

Fan

Hao

2021

Advances in Structural Engineering

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Vibration based structural health monitoring methods are usually dependent on the first several orders of modal information, such as natural frequencies, mode shapes and the related derived features. These information are usually in a low frequency range. These global vibration characteristics may not be sufficiently sensitive to minor structural damage. The alternative non-destructive testing method using piezoelectric transducers, called as electromechanical impedance (EMI) technique, has been developed for more than two decades. Numerous studies on the EMI based structural health monitoring have been carried out based on representing impedance signatures in frequency domain by statistical indicators, which can be used for damage detection. On the other hand, damage quantification and localization remain a great challenge for EMI based methods. Physics-based EMI methods have been developed for quantifying the structural damage, by using the impedance responses and an accurate numerical model. This article provides a comprehensive review of the exciting researches and sorts out these approaches into two categories: data-driven based and physics-based EMI techniques. The merits and limitations of these methods are discussed. In addition, practical issues and research gaps for EMI based structural health monitoring methods are summarized.

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Section: Data-driven Based Emi Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Review of piezoelectric impedance based structural health monitoring: Physics-based and data-driven methods

Fan

Hao

2021

Advances in Structural Engineering

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“…Fuzzy C-mean Clustering Fuzzy C-mean clustering is a common method in pattern recognition [12]. The eigenvalues of three principle components were clustered by fuzzy C-mean clustering and listed in Table 4.…”

Section: Recognition Of Wear Processmentioning

confidence: 99%

Failure Prediction and Wear State Evaluation of Power Shift Steering Transmission

Liu

Yan

et al. 2015

AMM

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Within the vehicle transmission, the friction surfaces of mechanical parts were consecutively worn-out and ultimately up to the degradation failures. For assessing the wear progress effectively, wear particles should be generally monitored by measuring the element concentration through Atomic emission (AE) spectroscopy. Herein, the spectral data sampled from life-cycle test has been processed by both the Principal Component Analysis (PCA) and further Kernel Principal Component Analysis (KPCA). Results show that KPCA acts more effectively in variable-dimensions reduction due to fewer principle components and higher cumulative contributing rate. To detect the threshold point at where the wear-stage upgraded, the Fuzzy C-means clustering algorithm was applied to process the eigenvalues of principle components. Furthermore, it is demonstrated that the principle components relate to the worn-out state of friction pairs or transmission parts. In general, the introduction of KPCA has contributed to assess the wear-stage at where the machine situates and the accurate worn-out state of various transmission parts.

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“…Although probabilistic neural networks (PNNs) have not been developed specifically for structural damage detection, their pattern matching capability makes them a very promising tool for classification problems [22,23]. Using the PNN learning and substructuring technique, rapid and accurate localization of damaged joints can be achieved.…”

Section: Introductionmentioning

confidence: 99%

Condition Assessment of Joints in Steel Truss Bridges Using a Probabilistic Neural Network and Finite Element Model Updating

2021

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The condition of joints in steel truss bridges is critical to railway operational safety. The available methods for the quantitative assessment of different types of joint damage are, however, very limited. This paper numerically investigates the feasibility of using a probabilistic neural network (PNN) and a finite element (FE) model updating technique to assess the condition of joints in steel truss bridges. A two-step identification procedure is developed to achieve damage localization and severity assessment. A series of FE models with single or multiple damages are simulated to generate the training and testing data samples and validate the effectiveness of the proposed approach. The influence of noise on the identification accuracy is also evaluated. The results show that the change rate of modal curvature (CRMC) can be used as a damage-sensitive input of the PNN and the accuracy of preliminary damage localization can exceed 90% when suitable training patterns are utilized. Damaged members can be localized in the correct substructure even with noise contamination. The FE model updating method used can effectively quantify the joint deterioration severity and is robust to noise.

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Fuzzy Cluster Analysis Methods Applied to Impedance Based Structural Health Monitoring for Damage Classification

Cited by 4 publications

References 17 publications

Review of piezoelectric impedance based structural health monitoring: Physics-based and data-driven methods

Review of piezoelectric impedance based structural health monitoring: Physics-based and data-driven methods

Failure Prediction and Wear State Evaluation of Power Shift Steering Transmission

Condition Assessment of Joints in Steel Truss Bridges Using a Probabilistic Neural Network and Finite Element Model Updating

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