Kappa-PSO-FAN based method for damage identification on composite structural health monitoring

Oliveira, Mário Anderson de; Araújo, Nelcileno; Inman, Daniel J.; Filho, Jozué Vieira

doi:10.1016/j.eswa.2017.11.022

Cited by 17 publications

(11 citation statements)

References 43 publications

(78 reference statements)

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“…The results showed that the SFAN-based method consumed less time for both training and testing phases when compared with PNN. This result was expected, as it is consistent with those from previous studies [ 36 , 37 ]. As a consequence, the SFAN-based method seems to be very reliable for implementing real-time SHM systems, especially for the testing phase by using an ordinary laptop.…”

Section: Discussionsupporting

confidence: 94%

“…For that, the PZT transducer is excited, and only its time response signal (voltage) is directly used to compute Euclidean Distances. It is important to remark that even analysis of the excitation signal has not shown that the time response signals are correlated with the electromechanical impedance (since it is guaranteed that the excitation input is kept constant) [ 37 , 50 ]. Hence, working on the time domain simplifies and speeds up the damage identification because the EMI is not computed, which by itself, is a great advantage in terms of processing time [ 37 , 50 ].…”

Section: Resultsmentioning

confidence: 99%

“…The laptop configuration consists of an Intel Core i3-2.2 GHz with 4 GB of RAM. Detailed analysis of the time consumption for PNN- and SFAN-based methods can be read in references [ 36 , 37 ].…”

Section: Discussionmentioning

confidence: 99%

“…As a result, they showed that SFAN parameters have a substantial influence on the SFAN performance. Hence, they proposed a method for choosing SFAN optimal parameters automatically [ 37 ].…”

Section: Introductionmentioning

confidence: 99%

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Use of Savitzky–Golay Filter for Performances Improvement of SHM Systems Based on Neural Networks and Distributed PZT Sensors

Oliveira¹,

Araújo

Silva³

et al. 2018

Sensors

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A considerable amount of research has focused on monitoring structural damage using Structural Health Monitoring (SHM) technologies, which has had recent advances. However, it is important to note the challenges and unresolved problems that disqualify currently developed monitoring systems. One of the frontline SHM technologies, the Electromechanical Impedance (EMI) technique, has shown its potential to overcome remaining problems and challenges. Unfortunately, the recently developed neural network algorithms have not shown significant improvements in the accuracy of rate and the required processing time. In order to fill this gap in advanced neural networks used with EMI techniques, this paper proposes an enhanced and reliable strategy for improving the structural damage detection via: (1) Savitzky–Golay (SG) filter, using both first and second derivatives; (2) Probabilistic Neural Network (PNN); and, (3) Simplified Fuzzy ARTMAP Network (SFAN). Those three methods were employed to analyze the EMI data experimentally obtained from an aluminum plate containing three attached PZT (Lead Zirconate Titanate) patches. In this present study, the damage scenarios were simulated by attaching a small metallic nut at three different positions in the aluminum plate. We found that the proposed method achieves a hit rate of more than 83%, which is significantly higher than current state-of-the-art approaches. Furthermore, this approach results in an improvement of 93% when considering the best case scenario.

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Section: Discussionsupporting

confidence: 94%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Use of Savitzky–Golay Filter for Performances Improvement of SHM Systems Based on Neural Networks and Distributed PZT Sensors

Oliveira¹,

Araújo

Silva³

et al. 2018

Sensors

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“…This is possible because the CNN applies several banks of filters in order to extract the best features that represent different structural conditions, in each frame. It potentially has direct application in the composite materials industry especially when applied to identify small damage and its progression as discussed in [ 22 , 24 , 61 ].…”

Section: Comparison With Other State-of-the-art Solutionsmentioning

confidence: 99%

A New Structural Health Monitoring Strategy Based on PZT Sensors and Convolutional Neural Network

Oliveira¹,

Monteiro²,

Filho

2018

Sensors

115

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Preliminaries convolutional neural network (CNN) applications have recently emerged in structural health monitoring (SHM) systems focusing mostly on vibration analysis. However, the SHM literature shows clearly that there is a lack of application regarding the combination of PZT-(lead zirconate titanate) based method and CNN. Likewise, applications using CNN along with the electromechanical impedance (EMI) technique applied to SHM systems are rare. To encourage this combination, an innovative SHM solution through the combination of the EMI-PZT and CNN is presented here. To accomplish this, the EMI signature is split into several parts followed by computing the Euclidean distances among them to form a RGB (red, green and blue) frame. As a result, we introduce a dataset formed from the EMI-PZT signals of 720 frames, encompassing a total of four types of structural conditions for each PZT. In a case study, the CNN-based method was experimentally evaluated using three PZTs glued onto an aluminum plate. The results reveal an effective pattern classification; yielding a 100% hit rate which outperforms other SHM approaches. Furthermore, the method needs only a small dataset for training the CNN, providing several advantages for industrial applications.

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Application of Supervised and Unsupervised Machine Learning to the Classification of Damaged Rotor‐Bearing Systems

Rezazadeh,

Felaco,

Fallahy

et al. 2023

Macromolecular Symposia

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In this letter, supervised machine learning classifiers are compared with clustering algorithms in the categorization of malfunctioned rotor systems. For this purpose, a dataset containing 660 faulted rotor‐bearing systems—that are, unbalanced, misaligned, and cracked—is used. Samples are created utilizing the finite element method in MATLAB. A sequential forward selection (SFS) method is employed to reduce the number of features in the signal‐processing stage after the feature extraction phase in the time, frequency, and time–frequency domains. The outcomes of three supervised algorithms—support vector machine, k‐nearest neighbors, and ensemble learning, as well as one unsupervised procedure, e.g., k‐Means clustering, are compared. In the latter method, to find the optimal number of clusters, the Calinski–Harabasz criterion is applied. The findings represent that, even though the supervised methods' acquired accuracies are noticeably higher (97.7% in the validation stage), using clustering algorithms can be beneficial in a variety of real‐time condition monitoring applications in rotating machines where the type, extent, and location of the damage are unknown.

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Kappa-PSO-FAN based method for damage identification on composite structural health monitoring

Cited by 17 publications

References 43 publications

Use of Savitzky–Golay Filter for Performances Improvement of SHM Systems Based on Neural Networks and Distributed PZT Sensors

Use of Savitzky–Golay Filter for Performances Improvement of SHM Systems Based on Neural Networks and Distributed PZT Sensors

A New Structural Health Monitoring Strategy Based on PZT Sensors and Convolutional Neural Network

Application of Supervised and Unsupervised Machine Learning to the Classification of Damaged Rotor‐Bearing Systems

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