Identification of multi-bolt head corrosion using linear and nonlinear shapelet-based acousto-ultrasonic methods

Wang, Furui

doi:10.1088/1361-665x/ac0f45

Cited by 9 publications

(8 citation statements)

References 54 publications

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“…In this case, we employ datasets from different flanges to respectively serve as training and test sets to study the method's robustness and its potential for practical application. As mentioned above, datasets (1,2,3,4) are captured from the flange A and datasets (5,6,7,8) are from the flange B. The trial settings are shown in Table 5, the performances of different methods are illustrated in Figure 11, and the confusion matrices are given in Figure 12.…”

Section: Resultsmentioning

confidence: 99%

“…However, such connection still suffers from problems, such as bolt looseness, which may be resulting from chemical erosion, mechanical vibration, impact from foreign objects, and improper installation. 3,4 That is, in the pipeline system, bolted connection represents a point of vulnerability and is prone to self-loosening due to uncertainties, which might lead to disastrous consequences, resulting in economic losses. Therefore, it is important to detect the bolt looseness of the subsea flange.…”

Section: Introductionmentioning

confidence: 99%

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Underwater bolted flange looseness detection using percussion-induced sound and Feature-reduced Multi-ROCKET model

Chen

Zheng

Zhu

et al. 2023

Structural Health Monitoring

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Recently, in the field of structural health monitoring, the detection of bolted connection looseness through percussion-based method and machine learning technology has received much attention due to the advantages of removing the requirement of sensor installation and potential for automation. However, there are few such research which are performed in the underwater environment. The paper proposes a new method, Feature-reduced Multiple Random Convolution Kernel Transform (FM-ROCKET), to identify the looseness level of the underwater bolted connections based on the percussion-induced sound (audio signal). By integrating deep learning (DL) and shallow learning, the FM-ROCKET model uses the 1D convolutional layer (a DL method) to extract features from the percussion-induced audio signal and adopts the rigid classifier (linear classifier, a shallow learning method) to classify the features. Five different preload levels of the bolted flange are considered. A hammer is utilized to tap the flange surface and the continuous percussion-induced audio signal is collected by a smartphone in an underwater environment. After the audio signal segmentation, single-hit audio signals are fed into the FM-ROCKET model. To verify the effectiveness of the proposed method, three case studies are conducted on two flanges. In case study I, the proposed method slightly outperforms other DL-based methods under different training/test splitting ratios. In case studies II and III, the proposed method is far more effective than other DL-based methods on independent and different test sets. The results demonstrate the superiority of the FM-ROCKET model in the underwater detection of bolted flange looseness. To the best of our knowledge, this article is the first attempt to address the detection of bolted flange looseness in the underwater environment by combining percussion-based method, DL, and shallow learning.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Underwater bolted flange looseness detection using percussion-induced sound and Feature-reduced Multi-ROCKET model

Chen

Zheng

Zhu

et al. 2023

Structural Health Monitoring

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“…Overall, considering that most signals captured in structural health monitoring (SHM) systems belong to time series, we may expect the potential of the dual-shapelet networks for damage detection in future work, which is more computation-friendly than deep learning based method. 41 Moreover, we may employ and enhance the shapelet-enhanced AE method to characterize other bolt failures, such as corrosion 42,43 and fatigue.…”

Section: Discussionmentioning

confidence: 99%

Multi-Bolt looseness detection using a new acoustic emission strategy

Wang

2022

Structural Health Monitoring

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In mechanical and aerospace engineering, different components are usually integrated together via bolted connections. Compared to the rivet joint and welding joint, the bolted connection is preferred in some cases due to its easy-to-operation and low-cost. However, the bolt self-loosening caused by vibration or other issues (e.g., improper installation and chemical corrosion) may induce severe accidents. Therefore, in this paper, the author proposes a new strategy based on the acoustic emission (AE) technique to detect bolt looseness. To the best of the author’s knowledge, this research is the first attempt to identify multi-bolt looseness via the AE-based method. Particularly, the main contribution is that the author proposes a new shapelet-enhanced AE method that employs a newly developed dual-shapelet networks classifier to discriminate AE waves. The dual-shapelet networks classifier consists of sample-specific shapelets, which is sensitive to the difference among various categories, and category-specific shapelets derived from auxiliary binary classifiers. The objective of category-specific shapelets is to address the imbalanced classification task, that is, discriminating minority categories. Then, the sample-specific shapelets and category-specific shapelets are combined to extract features from AE signals under different multi-bolt looseness cases, and the final classification is achieved by feeding the extracted features into a softmax layer. Finally, the author conducts an experiment to verify the effectiveness of the proposed method. Moreover, by comparing the proposed method’s performance with two baselines, the advantages of the shapelet-enhanced AE method can be demonstrated. Overall, this research demonstrates that the AE technique is valid to characterize friction and collision between asperities on the bolted interface, thus providing a new direction for multi-bolt looseness detection, and the proposed shapelet-enhanced AE method has substantial potential in the field of structural health monitoring (SHM).

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“…In the last decades, with the help of the rapid development and successful applications of time series analysis methods, many signal processing technologies, including filtering [21], correlation analysis [22], fast Fourier transform [23,24], shorttime Fourier transform [25,26], empirical mode decomposition (EMD) [27,28], ensemble EMD [29], variational mode decomposition (VMD) [30] and other methods [31][32][33][34][35][36], are implemented to enhance or extract the feature of the damage from the complex signals in Lamb wave-based SHMs. Moreover, some researches recently indicate that the singularities of the signal are related to some features of the structural damage, and these singularities are usually obtained by singular spectrum analysis (SSA).…”

Section: Introductionmentioning

confidence: 99%

Singular spectrum analysis and fuzzy entropy-based damage detection on a thin aluminium plate by using PZTs

Song

Ming

et al. 2022

Smart Mater. Struct.

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In this research, a new method based on singular spectrum analysis (SSA) and fuzzy entropy is developed for damage detection on thin wall-like structures, and the normalized fuzzy entropy is employed as an indicator to identify the severity of the damage. The Lead Zirconate Titanate (PZT) transducers are used in this research to generate and detect the Lamb waves. During the detection, the collected signals from the PZT sensors are firstly decomposed and reconstructed by SSA to extract the feature of the damage, and then the reconstructed signals with the feature of the damage are processed to obtain the normalized fuzzy entropy. An experimental setup of an aluminium plate with added magnets is fabricated to validate the proposed method. The experimental results show that when magnets are attached on the aluminium plate, the normalized fuzzy entropy is smaller than that when there are no magnets. That is because when magnets are placed on the plate, the movement and some vibration modes of Lamb waves are disturbed by the added magnets and this disturbing effect can be enhanced by increasing the number and locations of the added magnets, and eventually the complexity and nonlinearity of the waves are weakened. The experimental results of a single damage with different number of magnets indicate that the normalized fuzzy entropy decreases linearly as the number of the added magnets increases, which demonstrates that the proposed method can be used to detect the severity of the damage. Moreover, the experimental results of multi-damage on different locations indicate that the normalized fuzzy entropy is relevant with both the total number and locations of the added magnets. The normalized fuzzy entropy decreases linearly as the total number of the magnets increases, and the entropy of a single damage is smaller than that of the multi-damage with the same total number of magnets, which demonstrates that the proposed method also can be used for multi-damage detection on a thin plate. This study provides us a new approach to identifying a single or multiple damages on thin wall-like structures.

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Identification of multi-bolt head corrosion using linear and nonlinear shapelet-based acousto-ultrasonic methods

Cited by 9 publications

References 54 publications

Underwater bolted flange looseness detection using percussion-induced sound and Feature-reduced Multi-ROCKET model

Underwater bolted flange looseness detection using percussion-induced sound and Feature-reduced Multi-ROCKET model

Multi-Bolt looseness detection using a new acoustic emission strategy

Singular spectrum analysis and fuzzy entropy-based damage detection on a thin aluminium plate by using PZTs

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