Advances in Engineering Software最新文献_第2页

Multi-feature driven seismic damage state identification for reinforced concrete shear walls using computer vision and machine learning 利用计算机视觉和机器学习对钢筋混凝土剪力墙进行多特征地震破坏状态识别

IF 4 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Advances in Engineering Software

Pub Date : 2024-11-05 DOI: 10.1016/j.advengsoft.2024.103796

Samira Azhari , Amirali Mahmoodi , Amirhossein Samavi , Mohammadjavad Hamidia

In this paper, an image-based methodology using machine learning algorithms is developed for earthquake-induced damage state prediction in rectangular reinforced concrete shear walls. The machine learning models are developed using a database including experimental data points of 285 surface crack maps of damaged reinforced concrete shear walls collected from the literature. Eight different machine learning algorithms are utilized to train the damage-level classification models. The damage levels are defined according to the FEMA P-58 damage categories. In addition to the structural and geometric properties of the reinforced concrete shear walls with rectangular cross-section, three image-based indices including Succolarity, Lacunarity, and generalized fractal dimensions are measured as input features of the predictive models. Nine groups of features are selected as input for the machine learning algorithms. Using the GridsearchCV function, the hyperparameters resulting in the best algorithmic performance are chosen from a set of possible parameters. A five-fold cross-validation technique is applied to evaluate the models by resampling procedure. According to the results, the predictive model that uses the Extreme Gradient Boosting (XGB) algorithm with inputs that include both structural parameters and image indices performs best in terms of both overfitting prevention and classification accuracy. The outcomes of the damage state identification can be employed for safety assessment of the reinforced concrete buildings as well as repair/demolish decision-making after an earthquake.

本文采用机器学习算法，开发了一种基于图像的方法，用于矩形钢筋混凝土剪力墙的地震诱发损伤状态预测。机器学习模型的开发使用了一个数据库，其中包括从文献中收集的 285 幅受损钢筋混凝土剪力墙表面裂缝图的实验数据点。八种不同的机器学习算法被用来训练损伤等级分类模型。损坏等级是根据 FEMA P-58 损坏类别定义的。除了矩形截面钢筋混凝土剪力墙的结构和几何属性外，还测量了三个基于图像的指数，包括琥珀度（Succolarity）、漆度（Lacunarity）和广义分形维度（generalized fractal dimensions），作为预测模型的输入特征。选择了九组特征作为机器学习算法的输入。使用 GridsearchCV 函数，从一组可能的参数中选择出算法性能最佳的超参数。采用五倍交叉验证技术，通过重采样程序对模型进行评估。结果表明，使用极端梯度提升（XGB）算法的预测模型，其输入包括结构参数和图像指数，在防止过拟合和分类准确性方面表现最佳。损伤状态识别的结果可用于钢筋混凝土建筑的安全评估以及地震后的维修/拆除决策。

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引用次数: 0

A modified PODI-RBF method to improve the accuracy of local solutions for real-time finite element simulations of indenter contact problems 改进 PODI-RBF 方法，提高压头接触问题实时有限元模拟局部解的精度

IF 4 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Advances in Engineering Software

Pub Date : 2024-11-04 DOI: 10.1016/j.advengsoft.2024.103806

Hyeon-Gyeong Lee, Hyun-Gyu Kim

In this paper, a novel method is proposed to improve the accuracy of local solutions of the PODI-RBF method for real-time finite element (FE) simulations of indenter contact problems. In the offline stage, proper orthogonal decomposition (POD) basis vectors and coefficients are extracted from solution snapshots collected from full-order FE simulations of indenter contact problems with training contact locations and indentation depths. In the online stage, RBF interpolation is used to estimate POD basis vectors and coefficients for a new contact loading. Although the POD with interpolation (PODI) method using RBFs is very useful for obtaining FE solutions of indenter contact problems in real time, local solutions near a new contact location are less accurate when a new contact location is not close to the training contact locations. To improve the accuracy of local solutions near a new contact location, the first POD basis vector is replaced by the shifted first POD basis vector for the closest training contact location to the new contact location. Numerical results show that the modified PODI-RBF method is efficient and effective to achieve real-time FE simulations of indenter contact problems while improving the accuracy of local solutions near contact locations.

本文提出了一种新方法，以提高用于压头接触问题实时有限元（FE）模拟的 PODI-RBF 方法局部解的精度。在离线阶段，适当的正交分解（POD）基向量和系数是从训练接触位置和压痕深度的压头接触问题全阶有限元模拟的解快照中提取的。在联机阶段，使用 RBF 插值估计新接触载荷的 POD 基向量和系数。虽然使用 RBF 的插值 POD（PODI）方法对于实时获得压头接触问题的有限元求解非常有用，但当新接触位置与训练接触位置不相近时，新接触位置附近的局部求解精度较低。为了提高新接触位置附近局部解的精确度，第一 POD 基向量由与新接触位置最近的训练接触位置的移位第一 POD 基向量代替。数值结果表明，改进后的 PODI-RBF 方法能高效实现压头接触问题的实时有限元模拟，同时提高接触位置附近局部解的精度。

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引用次数: 0

A trimmed-NURBS-based thermal buckling isogeometric analysis framework for the variable stiffness plate with complex cutouts 基于修剪 NURBS 的带复杂切口的变刚度板热屈曲等距分析框架

IF 4 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Advances in Engineering Software

Pub Date : 2024-10-30 DOI: 10.1016/j.advengsoft.2024.103803

Haoqing Ding , Bingwen Qian , Yutao Hu , Changli Wang , Xin Zhang , Ruqi Sun , Bin Xu

The isogeometric analysis of variable-stiffness structures with curvilinear fibers has gained considerable research attention. However, dealing with structures that have complex cutouts poses challenges for isogeometric analysis. Additionally, the thermal-elastic behavior of variable-stiffness structures must be carefully considered, as they often operate in thermal environments. This study introduces a novel trimmed non-uniform rational basis spline (NURBS) method to address these challenges and investigate the thermal buckling behavior of variable-stiffness plates. The method generates trimmed NURBS elements using a level-set function on the initial NURBS mesh to describe complex geometries. Segmented density interpolation formulas are proposed to capture the contributions of different NURBS elements and to prevent localized eigenmodes. An artificial shear correction factor is introduced to mitigate shear locking. Several numerical examples with various boundary conditions and fiber configurations, are presented to demonstrate the high accuracy and low computational costs of the proposed method.

具有曲线纤维的变刚度结构的等距几何分析已获得了相当多的研究关注。然而，处理具有复杂切口的结构给等距分析带来了挑战。此外，由于变刚度结构通常在热环境中运行，因此必须仔细考虑其热弹性行为。本研究引入了一种新颖的修剪非均匀有理基础样条曲线（NURBS）方法来应对这些挑战，并研究变刚度板的热屈曲行为。该方法在初始 NURBS 网格上使用水平集函数生成修剪 NURBS 元素，以描述复杂的几何形状。提出了分段密度插值公式，以捕捉不同 NURBS 元素的贡献并防止局部特征模态。引入了人工剪切校正因子，以减轻剪切锁定。介绍了几个具有不同边界条件和纤维配置的数值示例，以证明所提方法的高精度和低计算成本。

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引用次数: 0

Concrete crack recognition and geometric parameter evaluation based on deep learning 基于深度学习的混凝土裂缝识别与几何参数评估

IF 4 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Advances in Engineering Software

Pub Date : 2024-10-30 DOI: 10.1016/j.advengsoft.2024.103800

Wang Shaowei, Xu Jiangbo, Wu Xiong, Zhang Jiajun, Zhang Zixuan, Chen Xinyu

Concrete cracks will greatly affect the normal use function of buildings. Traditional crack detection and image processing methods have problems such as large amounts of calculation and low detection accuracy. In this paper, the DeepLabV3+ network model is improved by introducing CBAM and ECANet attention mechanisms. The backbone stem module is changed to three 3 × 3 convolutions with larger receptive fields, and three low-level feature maps are extracted as input maps for the decoder to enhance semantic information, and finally form the C-E-DeepLabV3+ model. The method proposed in this paper is validated by integrating multiple typical crack image libraries such as Crack500. The results show that the MIoU value can reach 77.84 %, which is 4 %, 5.53 %, 6.52 %, 4.49 % and 3.44 % higher than the original model DeepLabV3+, advanced segmentation model YOLOv8x, classical segmentation models UNet, MobileNet and PSPNet, respectively. And in terms of model parameter amount, it is 39 % lower than the original DeepLabV3+ model, and compared to other traditional models, it is only slightly higher than the lightweight model MobileNet. On this basis, the orthogonal skeleton line method is used to calculate the length and width of segmented cracks. Compared with the actual measured values, the accuracy of the method in this paper can reach more than 93 %, which has good engineering applicability.

混凝土裂缝会严重影响建筑物的正常使用功能。传统的裂缝检测和图像处理方法存在计算量大、检测精度低等问题。本文通过引入 CBAM 和 ECANet 注意机制，改进了 DeepLabV3+ 网络模型。将骨干干模块改为三个具有更大感受野的 3 × 3 卷积，并提取三个底层特征图作为解码器的输入图，以增强语义信息，最终形成 C-E-DeepLabV3+ 模型。本文提出的方法通过整合 Crack500 等多个典型裂纹图像库进行了验证。结果表明，MIoU 值可以达到 77.84 %，分别比原始模型 DeepLabV3+、高级分割模型 YOLOv8x、经典分割模型 UNet、MobileNet 和 PSPNet 高出 4 %、5.53 %、6.52 %、4.49 % 和 3.44 %。而在模型参数量方面，它比原始模型 DeepLabV3+ 低 39%，与其他传统模型相比，仅略高于轻量级模型 MobileNet。在此基础上，采用正交骨架线法计算分割裂缝的长度和宽度。与实际测量值相比，本文方法的精度可达 93 % 以上，具有良好的工程适用性。

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引用次数: 0

An innovative method integrating two deep learning networks and hyperparameter optimization for identifying fiber optic temperature measurements in earth-rock dams 集成两个深度学习网络和超参数优化的创新方法，用于识别土石坝中的光纤温度测量值

IF 4 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Advances in Engineering Software

Pub Date : 2024-10-29 DOI: 10.1016/j.advengsoft.2024.103802

Lang Xu , Zhiping Wen , Huaizhi Su , Simonetta Cola , Nicola Fabbian , Yanming Feng , Shanshan Yang

Since one of the main threats to the safety of earth-rock dams is leakage, its timely and accurate identification is crucial. Distributed fiber optic sensing system (DFOS) is considered as one of the ideal methods for leakage monitoring in earth-rock dams. However, the working conditions of earth-rock dams are complex, and the identification of fiber optic temperature measurements has issues such as low efficiency and high misjudgment rate. For improving the identification efficiency and accuracy of fiber optic temperature measurements in earth-rock dams, a signal identification method integrating least squares generative adversarial network (LSGAN), one-dimensional convolutional neural network (1DCNN), and white shark optimization (WSO) algorithm is presented. Firstly, the LSGAN model is used to augment the signals of different categories to reduce the effect of data set unbalance on the identification result. According to the variation characteristics of fiber optic temperature measurement signals in earth-rock dams, a 1DCNN model is designed to extract signal features for classification. To reduce the blindness in hyperparameter setting of 1DCNN model, the WSO algorithm is introduced to optimize its key hyperparameters, which further enhances the identification accuracy of the model. The new method is applied to a data set specifically acquired with tests on a physical model of an earth-rock dam. The identification accuracy obtained with the new method reaches 99.76 %, which is better than the accuracy of other commonly used identification methods. Upon completion of the pre-training, the new method can fulfill the practical needs of fast identification and has promising applications.

土石坝安全的主要威胁之一是渗漏，因此及时准确地识别渗漏至关重要。分布式光纤传感系统（DFOS）被认为是土石坝渗漏监测的理想方法之一。然而，土石坝工况复杂，光纤测温识别存在效率低、误判率高等问题。为提高土石坝光纤测温的识别效率和精度，本文提出了一种集成最小二乘生成对抗网络（LSGAN）、一维卷积神经网络（1DCNN）和白鲨优化算法（WSO）的信号识别方法。首先，利用 LSGAN 模型对不同类别的信号进行增强，以减少数据集不平衡对识别结果的影响。根据土石坝光纤测温信号的变化特征，设计了 1DCNN 模型来提取信号特征进行分类。为减少 1DCNN 模型超参数设置的盲目性，引入 WSO 算法对其关键超参数进行优化，进一步提高了模型的识别精度。新方法被应用于对土石坝物理模型进行测试所获得的数据集。新方法的识别准确率达到 99.76%，优于其他常用识别方法。在完成预训练后，新方法可以满足快速识别的实际需要，具有广阔的应用前景。

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引用次数: 0

An open source MATLAB solver for contact finite element analysis 用于接触有限元分析的开源 MATLAB 求解器

IF 4 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Advances in Engineering Software

Pub Date : 2024-10-26 DOI: 10.1016/j.advengsoft.2024.103798

Bin Wang , Jiantao Bai , Shanbin Lu , Wenjie Zuo

Contact phenomenon widely exists in engineering, which is a high nonlinearity problem. However, the majority of open source contact finite element codes are written in C++, which are difficult for junior researchers to adopt and use. Therefore, this paper provides an open source 528-line MATLAB code and detailed interpretation for frictional contact finite element analysis considering large deformation, which is easy to learn and use by newcomers. This paper describes the contact projection, contact nodal forces and contact tangent stiffness matrices. The nonlinear equations are solved by the Newton–Raphson method. Numerical examples demonstrate the effectiveness of the MATLAB codes. The displacement, Cauchy stress and contact traction results are compared with the open-source software FEBIO.

接触现象广泛存在于工程中，是一个高非线性问题。然而，大多数开源的接触有限元代码都是用 C++ 编写的，对于初级研究人员来说很难采用和使用。因此，本文为考虑大变形的摩擦接触有限元分析提供了一个开源的 528 行 MATLAB 代码和详细解释，便于新手学习和使用。本文介绍了接触投影、接触节点力和接触切线刚度矩阵。非线性方程采用牛顿-拉夫逊法求解。数值示例证明了 MATLAB 代码的有效性。位移、Cauchy 应力和接触牵引结果与开源软件 FEBIO 进行了比较。

引用次数: 0

Multi-objective optimization of automotive seat frames using machine learning 利用机器学习对汽车座椅框架进行多目标优化

IF 4 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Advances in Engineering Software

Pub Date : 2024-10-24 DOI: 10.1016/j.advengsoft.2024.103797

Haifeng Chen, Ping Yu, Jiangqi Long

The optimal design of automobile seats plays an important role in passenger safety in high-speed accidents. In order to enhance the accuracy of the prediction of the input variables and output response of the seat, a hybrid machine learning prediction model that combines the improved gray wolf optimizer (IGWO) and back propagation neural network (BPNN) has been proposed, and the prediction effect of the model was validated using the seat simulation data. Initially, based on the experimental data, finite element models were developed for eight typical working conditions of automobile seats and their accuracy was validated. Subsequently, the energy absorption to mass ratio method was employed to screen the design variables, resulting in the selection of 17 thickness variables and 15 material variables. Thereafter, the gray wolf optimizer (GWO) algorithm underwent enhancement through the incorporation of the dynamic leadership hierarchy (DLH) mechanism and the revision of the positional formula, yielding the IGWO algorithm. Following this, the IGWO algorithm was applied to optimize the hyperparameters of BPNN, culminating in the establishment of the IGWO-BPNN model. Ultimately, the seat multi-objective optimization design process was addressed using multi-objective gray wolf optimizer (MOGWO) to achieve the Pareto frontier, while the decision-making was conducted using the combined compromise solution (CoCoSo) method to determine the best trade-off solution. Furthermore, the effectiveness of the proposed optimal design method is evidenced by comparing the baseline design, simulation analysis, and optimal design methods. The results indicate that the optimized automotive seat frame achieves a reduction in cost by 20.7 % and mass by 22.9 %, simultaneously maintaining safety performance. Consequently, the proposed optimization design methodology is demonstrated to be highly effective for the multi-objective optimization design of automotive seat frames.

汽车座椅的优化设计对高速事故中的乘客安全起着重要作用。为了提高座椅输入变量和输出响应的预测精度，提出了改进灰狼优化器（IGWO）和反向传播神经网络（BPNN）相结合的混合机器学习预测模型，并利用座椅仿真数据验证了模型的预测效果。首先，在实验数据的基础上，针对汽车座椅的八种典型工况建立了有限元模型，并验证了其准确性。随后，采用能量吸收与质量比的方法筛选设计变量，最终选择了 17 个厚度变量和 15 个材料变量。之后，灰狼优化器（GWO）算法通过纳入动态领导层次（DLH）机制和修改位置公式进行了改进，从而产生了 IGWO 算法。随后，IGWO 算法被用于优化 BPNN 的超参数，最终建立了 IGWO-BPNN 模型。最后，利用多目标灰狼优化器（MOGWO）解决了座椅多目标优化设计过程，以实现帕累托前沿，同时利用组合折衷方案（CoCoSo）方法进行决策，以确定最佳折衷方案。此外，通过比较基准设计、模拟分析和优化设计方法，证明了所提出的优化设计方法的有效性。结果表明，优化后的汽车座椅框架在保持安全性能的同时，成本降低了 20.7%，质量降低了 22.9%。因此，所提出的优化设计方法对汽车座椅框架的多目标优化设计非常有效。

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引用次数: 0

MGCHMO: A dynamic differential human memory optimization with Cauchy and Gauss mutation for solving engineering problems MGCHMO：利用考奇和高斯突变的动态微分人类记忆优化法解决工程问题

IF 4 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Advances in Engineering Software

Pub Date : 2024-10-22 DOI: 10.1016/j.advengsoft.2024.103793

Jialing Yan , Gang Hu , Bin Shu

The Human Memory Optimization (HMO) algorithm is a newly released metaheuristic algorithm based on humans in 2023, which can effectively solve most optimization problems. However, when dealing with complex optimization problems, HMO has limitations such as insufficient convergence accuracy and susceptibility to local optimal solutions. To this end, we integrated chaotic mapping, Cauchy mutation, Gaussian mutation, differential mutation, and parameter dynamic adjustment strategies into the original algorithm and developed an enhanced MGCHMO algorithm. Firstly, in the initialization phase of the MGCHMO, the Tent mapping chaotic mapping mechanism is introduced to enhance the diversity and search ability of the initial population through the traversal and randomness characteristics of chaos. Secondly, in the memory generation phase, we added the Cauchy mutation strategy, which effectively expanded the search range of the algorithm, helped the algorithm escape from local optima, and explored a broader solution space. Then, during the recall phase, Gaussian mutation and differential mutation are added. Among them, Gaussian mutation enables the algorithm to perform more refined searches within a local range. Differential mutation, on the other hand, guides the algorithm to explore towards a more optimal solution through the information of individual differences. Finally, the parameters of the algorithm are dynamically adjusted to enhance its optimization performance, ensuring that the algorithm maintains optimal search performance at different phases, thereby accelerating the convergence process and improving the quality of the solution.

To verify the optimization performance of MGCHMO, we conducted a series of detailed performance experiments on three different test sets: CEC2017, CEC2020, and CEC2022. The results showed that MGCHMO has higher convergence and stability. In addition, we tested the applicability of MGCHMO on 30 engineering examples, topology optimization design, aerospace orbit optimization, and curve shape optimization, and the results further demonstrated the significant application capability and feasibility of MGCHMO.

人类记忆优化算法（HMO）是 2023 年新发布的一种基于人类的元启发式算法，可以有效解决大多数优化问题。然而，在处理复杂的优化问题时，HMO存在收敛精度不够、易出现局部最优解等局限性。为此，我们将混沌映射、考奇突变、高斯突变、微分突变、参数动态调整等策略集成到原算法中，开发了增强型MGCHMO算法。首先，在MGCHMO的初始化阶段，引入了Tent映射混沌映射机制，通过混沌的遍历性和随机性增强初始种群的多样性和搜索能力。其次，在记忆生成阶段，我们加入了 Cauchy 突变策略，有效地扩大了算法的搜索范围，帮助算法摆脱局部最优，探索更广阔的解空间。然后，在召回阶段，增加了高斯突变和微分突变。其中，高斯突变能让算法在局部范围内进行更精细的搜索。而差分突变则通过个体差异信息引导算法探索更优化的解决方案。最后，对算法参数进行动态调整，以提高其优化性能，确保算法在不同阶段保持最佳搜索性能，从而加快收敛过程，提高解的质量。为了验证 MGCHMO 的优化性能，我们在三个不同的测试集上进行了一系列详细的性能实验：为了验证 MGCHMO 的优化性能，我们在三个不同的测试集上进行了一系列详细的性能实验：CEC2017、CEC2020 和 CEC2022。结果表明，MGCHMO 具有更高的收敛性和稳定性。此外，我们还在 30 个工程实例、拓扑优化设计、航空航天轨道优化和曲线形状优化中测试了 MGCHMO 的适用性，结果进一步证明了 MGCHMO 的显著应用能力和可行性。

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引用次数: 0

A two-step approach for damage identification in bridge structure using convolutional Long Short-Term Memory with augmented time-series data 利用增强时间序列数据的卷积长短期记忆识别桥梁结构损伤的两步法

IF 4 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Advances in Engineering Software

Pub Date : 2024-10-19 DOI: 10.1016/j.advengsoft.2024.103795

Lan Nguyen-Ngoc , Hoa Tran-Ngoc , Thang Le-Xuan , Chi-Thanh Nguyen , Guido De Roeck , Thanh Bui-Tien , Magd Abdel Wahab

This paper presents a novel two-step approach to identifying structural damages in bridge structure through the integration of 1D Convolutional Neural Network (1DCNN) and Long Short-Term Memory (LSTM) networks, enhanced by the augmentation and transformation techniques using Symbolic Aggregate approXimation (SAX) for time-series data analysis. In the first step, the time-series data of the bridge is diversified and quantified by augmentation techniques to make the model more robust and increase its generalization capabilities. After that, SAX is implemented to reduce the volume and categorize time series data through the transformation of continuous time series into discrete symbols, thereby decreasing the size of the data for more efficient training performance. In the second step, an advanced DL model combining 1DCNN and LSTM is proposed to tackle the damage identification problems of the processed data. By leveraging the strengths of CNNs in feature extraction and LSTMs in sequence learning, combined with advanced techniques for data augmentation, our methodology offers a robust solution not only for improving the model's training process but also for enabling it to learn from a more diverse and comprehensive dataset that mimics different damage scenarios, allowing more accurate detection of damages within bridge structures. Validation of the proposed method is conducted using time-series data collected from Chuong Duong Bridge structure. The effectiveness of the proposed method is compared with other models, such as 1DCNN, LSTM, and the combined 1DCNN-LSTM. The results show that the proposed 1DCNN-LSTM-SAX outperforms the other methods in terms of accuracy and, thus, can be used extensively to deal with the damage identification problems of bridges using time-series data.

本文介绍了一种分两步识别桥梁结构损伤的新方法，该方法通过整合一维卷积神经网络（1DCNN）和长短期记忆（LSTM）网络，并利用符号聚合估计（SAX）的增强和转换技术对时间序列数据进行分析。第一步，通过增强技术对桥梁的时间序列数据进行多样化和量化，使模型更加稳健，并提高其泛化能力。之后，通过将连续时间序列转换为离散符号，实现 SAX，以减少数据量并对时间序列数据进行分类，从而减少数据量，提高训练效率。第二步，提出了结合 1DCNN 和 LSTM 的先进 DL 模型，以解决处理数据的损坏识别问题。通过利用 CNN 在特征提取方面的优势和 LSTM 在序列学习方面的优势，并结合先进的数据增强技术，我们的方法提供了一种稳健的解决方案，不仅能改进模型的训练过程，还能使其从更多样、更全面的数据集中学习，模拟不同的损坏情况，从而更准确地检测桥梁结构的损坏情况。我们使用从忠阳大桥结构中收集的时间序列数据对所提出的方法进行了验证。将所提方法的有效性与其他模型（如 1DCNN、LSTM 和 1DCNN-LSTM 组合模型）进行了比较。结果表明，所提出的 1DCNN-LSTM-SAX 在准确性方面优于其他方法，因此可广泛用于处理使用时间序列数据的桥梁损坏识别问题。

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引用次数: 0

Multi-size wide kernel convolutional neural network for bearing fault diagnosis 用于轴承故障诊断的多尺寸宽核卷积神经网络

IF 4 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Advances in Engineering Software

Pub Date : 2024-10-19 DOI: 10.1016/j.advengsoft.2024.103799

Prashant Kumar , Izaz Raouf , Jinwoo Song , Prince , Heung Soo Kim

The bearing is an indispensable part of mechanical systems. Fault diagnosis of bearing faults is vital for uninterrupted operations of the system, and to prevent catastrophic failure. Artificial intelligence implementation has revolutionized the bearing fault diagnosis method. Application of deep learning has eliminated manual feature extraction and selection requirements. While conventional convolutional neural networks have demonstrated potential in diagnosing faults, considering a more extensive variety of spatial variables can further optimize their performance. This paper proposes a multi-wide-kernel convolutional neural network-based model for bearing fault diagnosis. We propose wide kernels in the neural network's convolutional layers, which enable the model to learn broader patterns from the input for bearing fault diagnosis. The wide-kernel design enables the network to obtain local and global features more effectively, improving the network's capacity to distinguish between healthy and faulty bearings. We train and validate the proposed multi-wide-kernel convolutional neural networks using an extensive dataset of vibration signals collected from bearings under diverse scenarios. Because of its increased sensitivity to subtle fault patterns, the proposed model offers better accuracy. The model's efficacy is further confirmed by comparing it with existing cutting-edge techniques for diagnosing bearing faults.

轴承是机械系统不可或缺的组成部分。轴承故障诊断对系统不间断运行和防止灾难性故障至关重要。人工智能的应用彻底改变了轴承故障诊断方法。深度学习的应用消除了人工特征提取和选择的要求。虽然传统的卷积神经网络在故障诊断方面已显示出潜力，但考虑更广泛的空间变量可以进一步优化其性能。本文提出了一种基于多宽核卷积神经网络的轴承故障诊断模型。我们在神经网络的卷积层中提出了宽核，使模型能够从轴承故障诊断的输入中学习更广泛的模式。宽核设计使网络能更有效地获取局部和全局特征，从而提高网络区分健康轴承和故障轴承的能力。我们使用从不同场景下的轴承收集到的大量振动信号数据集，对所提出的多宽核卷积神经网络进行了训练和验证。由于对细微故障模式的敏感度提高，所提出的模型具有更高的准确性。通过与现有的轴承故障诊断尖端技术进行比较，进一步证实了该模型的有效性。

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引用次数: 0