检索结果-内蒙古大学图书馆

data anomaly detection for structural health monitoring using the Mixture of Bridge Experts

STRUCTURES 2025年 71卷

作者： Hao, Changshun Gong, Yu Liu, Baodong Pan, Zhenhua Sun, Wupeng Li, Yan Zhuo, Yi Ma, Yongpeng Zhang, Linlin Beijing Jiaotong Univ Sch Civil Engn Beijing 100044 Peoples R China China Railway Design Corp Tianjin 300000 Peoples R China Beijing Inst Technol Sch Mechatron Engn Beijing 100081 Peoples R China China Railway Shanghai Grp Co Ltd Shanghai 200071 Peoples R China

Structure health monitoring systems (SHMs) play a crucial role in understanding the condition of structures. However, owing to various uncertain factors, sensor data may be anomalous, posing a great challenge to the realtime capture of dynamic characteristics of bridges. Hence, detecting anomalous data is crucial for SHM systems. This paper proposes a lightweight model to enable SHMs to detect anomalous data automatically and efficiently. This method combines three modules: Bridge Signal Transformer (BST), Mobile Vision Transformer (MobileViT), and Mixture of Bridge Experts (MoBE). Firstly, the acceleration data is converted to an image, and the MobileViT module is used to detect the anomalous data in the converted image. Since the converted image loses the absolute information of the data, this paper proposes a BST module, which uses directly the information of the data to identify the data type. MoBE module combines perfectly the advantages of the two modules to detect various types of anomalous data accurately. Experiments on an acceleration dataset from an extra-long-span railway cable-stayed bridge validate the advantages of the proposed method. This method performs well in any period, proving its generalization ability.

关键词： Bridge engineering Structural health monitoring data anomaly detection Mixture of Bridge Experts

来源：评论

学校读者我要写书评

暂无评论

data anomaly detection for structural health monitoring using a combination network of Ganomaly and CNN

引用

SMART STRUCTURES AND SYSTEMS 2022年第1期29卷 53-62页

作者： Liu, Gaoyang Niu, Yanbo Zhao, Weijian Duan, Yuanfeng Shu, Jiangpeng Zhejiang Univ Coll Civil Engn & Architecture Hangzhou 310058 Peoples R China Zhejiang Univ Ctr Balance Architecture Hangzhou 310058 Peoples R China Zhejiang Univ Co Ltd Architectural Design & Res Inst Hangzhou 310058 Peoples R China

The deployment of advanced structural health monitoring (SHM) systems in large-scale civil structures collects large amounts of data. Note that these data may contain multiple types of anomalies (e.g., missing, minor, outlier, etc.) caused by harsh environment, sensor faults, transfer omission and other factors. These anomalies seriously affect the evaluation of structural performance. Therefore, the effective analysis and mining of SHM data is an extremely important task. Inspired by the deep learning paradigm, this study develops a novel generative adversarial network (GAN) and convolutional neural network (CNN)-based data anomaly detection approach for SHM. The framework of the proposed approach includes three modules : (a) A three-channel input is established based on fast Fourier transform (FFT) and Gramian angular field (GAF) method;(b) A Ganomaly is introduced and trained to extract features from normal samples alone for class-imbalanced problems;(c) Based on the output of Ganomaly, a CNN is employed to distinguish the types of anomalies. In addition, a dataset-oriented method (i.e., multistage sampling) is adopted to obtain the optimal sampling ratios between all different samples. The proposed approach is tested with acceleration data from an SHM system of a long-span bridge. The results show that the proposed approach has a higher accuracy in detecting the multi-pattern anomalies of SHM data.

关键词： convolutional neural network data anomaly detection generative adversarial network Gramian angular field long-span bridge structural health monitoring

来源：评论

学校读者我要写书评

暂无评论

data anomaly detection for Bridge SHM Based on CNN Combined with Statistic Features

引用

JOURNAL OF NONDESTRUCTIVE EVALUATION 2022年第1期41卷 28-28页

作者： Zhang, Han Lin, Jing Hua, Jiadong Gao, Fei Tong, Tong Beihang Univ Sch Reliabil & Syst Engn Xueyuan Rd 37 Beijing Peoples R China Beihang Univ Sci & Technol Reliabil & Environm Engn Lab Xueyuan Rd 37 Beijing Peoples R China Beihang Univ Adv Mfg Ctr Ningbo Inst Technol Ningbo 315100 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

Structural health monitoring of long-span bridge has received increasing attention in recent years. In order to achieve accurate monitoring, the integrity of data collection should be guaranteed. Unfortunately, these data inevitably contain a variety of types of anomalies due to sensor faults, harsh environments, and other issues. Identifying anomalies from the data is essential to ensure credible monitoring results. Machine learning methods have the potential to detect data anomaly automatically. However, a well-performed convolutional neural network (CNN) model requires a large number of balanced training samples and frequent tuning. When similar anomalous patterns exist in complex systems, the tuning process might be time-consuming. In this paper, a data anomaly detection method is proposed based on CNN combined with statistic features. Firstly, acceleration data are downsampled, stacked, and input into CNN as the training set. A CNN model is designed and trained. Intermediate results are obtained through the model. Subsequently, the statistic features are applied to analyze and classify the confusable patterns. The novelty of this framework is that it combines the advantages of CNN and statistical features, which can realize data anomaly detection faster and more accurately than using CNN alone. The results of the acceleration data from a bridge demonstrate the effectiveness of the proposed approach to identify the anomalous data.

关键词： data anomaly detection Convolutional neural network Statistic features Structural health monitoring Long-span bridge

来源：评论

学校读者我要写书评

暂无评论

A Power data anomaly detection Model Based on Deep Learning with Adaptive Feature Fusion

引用

Computers, Materials & Continua 2024年第6期79卷 4045-4061页

作者： Xiu Liu Liang Gu Xin Gong Long An Xurui Gao Juying Wu State Grid Information&Telecommunication Co.of SEPC Big Data CenterTaiyuan030000China

With the popularisation of intelligent power,power devices have different shapes,numbers and *** means that the power data has distributional variability,the model learning process cannot achieve sufficient extraction of data features,which seriously affects the accuracy and performance of anomaly ***,this paper proposes a deep learning-based anomaly detection model for power data,which integrates a data alignment enhancement technique based on random sampling and an adaptive feature fusion method leveraging dimension *** at the distribution variability of power data,this paper developed a sliding window-based data adjustment method for this model,which solves the problem of high-dimensional feature noise and low-dimensional missing *** address the problem of insufficient feature fusion,an adaptive feature fusion method based on feature dimension reduction and dictionary learning is proposed to improve the anomaly data detection accuracy of the *** order to verify the effectiveness of the proposed method,we conducted effectiveness comparisons through elimination *** experimental results show that compared with the traditional anomaly detection methods,the method proposed in this paper not only has an advantage in model accuracy,but also reduces the amount of parameter calculation of the model in the process of feature matching and improves the detection speed.

关键词： data alignment dimension reduction feature fusion data anomaly detection deep learning

来源：评论

学校读者我要写书评

暂无评论

Deep learning-based data anomaly detection for highway slope structural health monitoring: A comparative study

引用

TRANSPORTATION GEOTECHNICS 2025年 51卷

作者： Dong, Shi Long, Zhiyou Zhang, Shiyuan Wang, Jianwei Zuo, Chen Yang, Chao Jiang, Jinyi Cui, Zhiwei Wan, Zhaolong Changan Univ Sch Transportat Engn Xian 710064 Peoples R China Changan Univ Engn Res Ctr Highway Infrastruct Digitalizat Minist Educ PRC Xian 710064 Peoples R China Zhejiang Sci Res Inst Transport Hangzhou 310023 Peoples R China Shaanxi Expressway Engn Testing Inspect & Testing Xian 710086 Peoples R China Zhejiang Inst Commun Zhejiang Engn Res Ctr Digital Highway Appl Technol Hangzhou 311112 Peoples R China

Highway slope instability has a significant influence on traffic safety. However, there are many anomalies in slope SHM data, which is critical to timely warnings and safety assessments of slopes. In this paper, we carried out a multi-case comparative study of deep learning models to examine the recognition accuracy of anomalous data. First, our program collected the monitoring data from the Baihe and Lueyang slopes of the Shi-Tian expressway in Shaanxi province. Six categories of abnormal data were found using K-means clustering. Second, based on the time response images, the frequency response and Gramian Angular Field (GAF) images were superimposed to improve the model's ability to identify six types of data anomalies. Third, we employed combo loss to tackle the data imbalance problem by incorporating dice loss and focal loss. Last, we conducted a comparative study of nine deep learning models to investigate the anomaly detection capability with the combo loss function. The results indicated that a combination of frequency response and GAF images can effectively improve the identification of abnormal data. The combo loss with equal weights significantly improved specific anomaly categories' performance. In addition, the performance of ResNet50, EfficientNetB1, and Vision Mamba exhibited impressive classification accuracy, generalization ability, and computational efficiency. Our investigation has the potential to recognize slope SHM data abnormal types in various scenarios, further improving the precision of subsequent slope capability analysis.

关键词： Slope structural health monitoring (SHM) Big data data anomaly detection Deep learning Comparative study

来源：评论

学校读者我要写书评

暂无评论

Transfer learning-based data anomaly detection for structural health monitoring

引用

STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2023年第5期22卷 3077-3091页

作者： Pan, Qiuyue Bao, Yuequan Li, Hui Harbin Inst Technol Minist Ind & Informat Technol Key Lab Smart Prevent & Mitigat Civil Engn Disaste Harbin Peoples R China Harbin Inst Technol Minist Educ Key Lab Struct Dynam Behav & & Control Harbin Peoples R China Harbin Inst Technol Sch Civil Engn Harbin Peoples R China Harbin Inst Technol Sch Civil Engn 73 Huanghe Rd Harbin 150001 Peoples R China

The structural health monitoring (SHM) data of civil infrastructure are inevitably contaminated due to sensor faults, environmental noise interference, and data transmission failures. Anomalous data severely disturb the subsequent structural modal identification, damage identification, and condition assessment. Therefore, it is critical to detect and clean SHM data before data analysis. This paper proposes a novel approach for data anomaly detection based on transfer learning, that makes full use of the similarity of the anomalous patterns across different bridges and shares the knowledge incorporated in a deep neural network to achieve high-accuracy data anomaly identification for bridge groups. In the proposed approach, first, a multivariate database for a source bridge is built by plotting and labeling the raw sequential data. Then, a convolutional neural network (CNN) for data anomaly classification is designed and trained with the database in different conditions. The original CNN with the highest accuracy is transferred to a new bridge with enhancement training using a small part of the target bridge data. To validate the performance of the proposed method, the multivariate SHM data for two real long-span bridges are employed, including the acceleration, strain, displacement, humidity, and temperature data. The results demonstrate that transfer learning leads to a better classification capacity for the case of scarce labeled training data compared with the original network.

关键词： Structural health monitoring data anomaly detection transfer learning convolutional neural network multivariate data

来源：评论

学校读者我要写书评

暂无评论

data anomaly detection for Structural Health Monitoring Based on a Convolutional Neural Network

引用

SENSORS 2023年第20期23卷 8525页

作者： Kim, Soon-Young Mukhiddinov, Mukhriddin Gachon Univ Dept Phys Educ Seongnam 13120 South Korea Univ Management & Future Technol Dept Commun & Digital Technol Tashkent 100208 Uzbekistan

Structural health monitoring (SHM) has been extensively utilized in civil infrastructures for several decades. The status of civil constructions is monitored in real time using a wide variety of sensors;however, determining the true state of a structure can be difficult due to the presence of abnormalities in the acquired data. Extreme weather, faulty sensors, and structural damage are common causes of these abnormalities. For civil structure monitoring to be successful, abnormalities must be detected quickly. In addition, one form of abnormality generally predominates the SHM data, which might be a problem for civil infrastructure data. The current state of anomaly detection is severely hampered by this imbalance. Even cutting-edge damage diagnostic methods are useless without proper data-cleansing processes. In order to solve this problem, this study suggests a hyper-parameter-tuned convolutional neural network (CNN) for multiclass unbalanced anomaly detection. A multiclass time series of anomaly data from a real-world cable-stayed bridge is used to test the 1D CNN model, and the dataset is balanced by supplementing the data as necessary. An overall accuracy of 97.6% was achieved by balancing the database using data augmentation to enlarge the dataset, as shown in the research.

关键词： CNN data anomaly detection time-series classification structural health monitoring sensor technology deep learning

来源：评论

学校读者我要写书评

暂无评论

A novel and robust data anomaly detection framework using LAL-AdaBoost for structural health monitoring

引用

JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING 2022年第2期12卷 305-321页

作者： Xu, Jie Dang, Dazhi Ma, Qian Liu, Xuan Han, Qinghua Tianjin Univ Sch Civil Engn Tianjin 300350 Peoples R China Tianjin Univ Key Lab Earthquake Engn Simulat & Seism Resilienc China Earthquake Adm Tianjin 300350 Peoples R China Tianjin Univ Minist Educ Key Lab Coast Civil Struct Safety Tianjin 300350 Peoples R China

The development of structural health monitoring (SHM) on civil infrastructures has resulted in enormous amount of acquired data along with the pressure of data processing and data mining. Abnormalities in data can lead to serious analytical error in later assessment. Such anomalous data patterns generally account for a relatively small portion of the overall dataset, which can be easily misclassified as normal data by regular classifiers. In this paper, a novel and robust data anomaly detection framework was proposed. The core novelty in this framework is the utilization of learning active learning (LAL) and AdaBoost algorithm aiming to reduce the costly manual work of labeling and improve the classification of anomaly patterns. Furthermore, the problem of biased classification brought by imbalanced datasets has also been solved by the LAL. Wavelet packet transform was also utilized to extract features from the acceleration data. The methodologies were firstly introduced precisely in this paper followed by two study cases to verify the feasibility of the proposed framework for data anomaly detection. The first case was a dataset with the anomalies synthetically added to the acceleration time history data measured in dynamic tests of a grid structure, including five kinds of data abnormalities. Both the balanced and imbalanced datasets were studied and analyzed, where a comparative study was carried out between the LAL-AdaBoost and uncertainty sampling-based AdaBoost with the same training and testing sets. The results showed that LAL-AdaBoost outperformed in both scenarios with higher accuracies and faster convergence speed. Then, a further study was carried out using acceleration data collected from a long-span bridge. By querying only limited amount of the training set, the proposed framework could accurately detect and classify 97.95% anomaly patterns of the testing set, showing great potential for further and broader application in the field of SHM data processi

关键词： data anomaly detection Structural health monitoring Active learning AdaBoost

来源：评论

学校读者我要写书评

暂无评论

Convolutional neural network-based data anomaly detection considering class imbalance with limited data

引用

SMART STRUCTURES AND SYSTEMS 2022年第1期29卷 63-75页

作者： Du, Yao Li, Ling-fang Hou, Rong-rong Wang, Xiao-you Tian, Wei Xia, Yong Hong Kong Polytech Univ Dept Civil & Environm Engn Hong Kong Peoples R China

The raw data collected by structural health monitoring (SHM) systems may suffer multiple patterns of anomalies, which pose a significant barrier for an automatic and accurate structural condition assessment. Therefore, the detection and classification of these anomalies is an essential pre-processing step for SHM systems. However, the heterogeneous data patterns, scarce anomalous samples and severe class imbalance make data anomaly detection difficult. In this regard, this study proposes a convolutional neural network-based data anomaly detection method. The time and frequency domains data are transferred as images and used as the input of the neural network for training. ResNet18 is adopted as the feature extractor to avoid training with massive labelled data. In addition, the focal loss function is adopted to soften the class imbalance-induced classification bias. The effectiveness of the proposed method is validated using acceleration data collected in a long-span cable-stayed bridge. The proposed approach detects and classifies data anomalies with high accuracy.

关键词： class imbalance data anomaly detection focal loss limited labelled data transfer learning

来源：评论

学校读者我要写书评

暂无评论

CNN based data anomaly detection using multi-channel imagery for structural health monitoring

引用

SMART STRUCTURES AND SYSTEMS 2022年第1期29卷 181-193页

作者： Shajihan, Shaik Althaf, V Wang, Shuo Zhai, Guanghao Spencer, Billie F., Jr. Univ Illinois Dept Civil & Environm Engn Urbana IL 61801 USA

data-driven structural health monitoring (SHM) of civil infrastructure can be used to continuously assess the state of a structure, allowing preemptive safety measures to be carried out. Long-term monitoring of large-scale civil infrastructure often involves data-collection using a network of numerous sensors of various types. Malfunctioning sensors in the network are common, which can disrupt the condition assessment and even lead to false-negative indications of damage. The overwhelming size of the data collected renders manual approaches to ensure data quality intractable. The task of detecting and classifying an anomaly in the raw data is non-trivial. We propose an approach to automate this task, improving upon the previously developed technique of image-based pre-processing on one-dimensional (1D) data by enriching the features of the neural network input data with multiple channels. In particular, feature engineering is employed to convert the measured time histories into a 3 channel image comprised of (i) the time history, (ii) the spectrogram, and (iii) the probability density function representation of the signal. To demonstrate this approach, a CNN model is designed and trained on a dataset consisting of acceleration records of sensors installed on a long-span bridge, with the goal of fault detection and classification. The effect of imbalance in anomaly patterns observed is studied to better account for unseen test cases. The proposed framework achieves high overall accuracy and recall even when tested on an unseen dataset that is much larger than the samples used for training, offering a viable solution for implementation on full-scale structures where limited labeled-training data is available.

关键词： convolutional neural network (CNN) data anomaly detection sensor-fault identification structural health monitoring

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：