A three-stage online anomaly identification model for monitoring data in dams

Xu, Ying; Huang, Huibao; Li, Yanling; Zhou, Jingren; Lü, Xiang; Wang, Yongfei

doi:10.1177/14759217211025766

Cited by 13 publications

(5 citation statements)

References 26 publications

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“…The reasons for abnormal monitoring data include environmental quantity response, measurement error, and characterization of structural deterioration (Xu et al, 2021). Environmental quantity response is the real response of the seepage field of tailings dam caused by environmental changes such as rainfall, reservoir water level, earthquake and tailings discharge.…”

Section: Distinction Methods Of Environmental Quantity Responsementioning

confidence: 99%

Anomaly identification of monitoring data and safety evaluation method of tailings dam

Dong

Yang²,

Yan³

et al. 2022

Front. Earth Sci.

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The seepage field of tailings dam is closely related to the safety state. Real-time evaluation of seepage field safety based on monitoring data is of great significance to ensure the safe operation of tailings pond. The premise of accurately evaluating the safety status is to ensure reliability of the data, and it is necessary to identify the anomalies of the monitoring data. Because of the complex influence factors of seepage field of tailings dam, the traditional anomaly identification method based on regression model fails due to its low fitting accuracy. Therefore, a novel abnormal identification method of monitoring data based on improved cloud model and radial basis function neural network model, which can accurately identify anomaly data and distinguish the environmental quantity response. Based on the coupling relationship between the seepage field and the slope stability, the surrogate model between the depth of saturation line and the safety factor of slope stability is constructed, and the real-time safety evaluation method of seepage field is put forward. The proposed methods are applied to an engineering example. The misjudgment rates of the abnormal data identification method are less than 5%, and it has better applicability than the traditional regression model. The constructed real-time safety evaluation model accurately reflected the health status of the seepage field, and realized the quantitative assessment of the safety of tailings dam. This provides reliable data support for the operation management and the risk control of tailings pond.

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Section: Distinction Methods Of Environmental Quantity Responsementioning

confidence: 99%

Anomaly identification of monitoring data and safety evaluation method of tailings dam

Dong

Yang²,

Yan³

et al. 2022

Front. Earth Sci.

Self Cite

View full text Add to dashboard Cite

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“…e finite element method can also be used to calculate the safe deformation range of the dam body, and the results can be used as the threshold. is process is typically considered complex for determining the threshold, and it is usually necessary to determine the threshold based on expert experience and a comparison of similar projects [25,26].…”

Section: Other Methodsmentioning

confidence: 99%

Outlier Detection of Gravity Dam Deformation Monitoring Data Based on the Multiple Local Outlier Coefficient Method

Bin

Bai

et al. 2022

Mathematical Problems in Engineering

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In the deformation monitoring data of gravity dams, a few outliers are often found, which will have adverse effects on the monitoring of model building and other data analysis work. In this study, the multiple local outlier coefficient method was proposed to quickly detect the outlier data in real time and to provide high-quality data for subsequent data analysis. This method was based on the idea of distance in the outlier detection algorithm, aiming at laws and characteristics of gravity dam deformation monitoring data. First, the basic principle, calculation steps, and basic features of the multiple local outlier coefficient method were studied. Then, for the two important parameters of the algorithm, the appropriate window length was selected using autocorrelation and partial autocorrelation analysis, and the appropriate threshold values were selected using the 3σ criterion, maximum method, and empirical method. Finally, an engineering example was used to verify that the algorithm could accurately detect the outliers in the gravity dam deformation monitoring data, and the deviation degree and meaning of the outliers were understood according to the calculated outlier coefficient. The multiple local outlier coefficient method has the advantages of a simple calculation principle, fast calculation speed, real-time detection, and a clear meaning of calculation results. By selecting the appropriate parameters, the method could satisfy the outlier detection of different types of data, offering an advantage in adapting to the computing demand of massive monitoring data and improving the intelligence and real-time monitoring of dam safety.

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“…These prediction methods each have their own characteristics and can be selected and applied according to different application scenarios and needs. However, in practical applications, a single prediction method often fails to fully and accurately reflect the safety status of tunnels [11]. Therefore, it is necessary to comprehensively consider multiple factors and adopt a combination of multiple methods for tunnel safety monitoring and early warning.…”

Section: Introductionmentioning

confidence: 99%

Intelligent early warning of highway tunnel safety monitoring data based on Markov model

Wang

2024

International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024)

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Traditional highway tunnel management systems often face problems such as independent and decentralized management, inflexible monitoring, and low visualization of disease details. Especially in the face of traffic accidents, lane foreign objects, vehicle congestion, and vehicle and pedestrian congestion in tunnels, traditional warning systems often fail to provide timely and accurate warning information, posing great risks to the safe operation of tunnels. In response to the above issues, this article proposes an intelligent early warning system for highway tunnel safety monitoring data based on Markov model (MM). This system fully utilizes the advantages of MM in describing random processes, and achieves dynamic monitoring and early warning of tunnel safety status through real-time analysis of tunnel monitoring data, combined with Internet of Things (IoT) technology. The experimental results show that the intelligent early warning system for highway tunnel safety monitoring data based on MM has high accuracy and reliability. It can not only achieve real-time monitoring and early warning of tunnel safety status, but also provide rich visual information to help managers better understand the operation status of tunnels and improve the level of tunnel safety management.

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A three-stage online anomaly identification model for monitoring data in dams

Cited by 13 publications

References 26 publications

Anomaly identification of monitoring data and safety evaluation method of tailings dam

Anomaly identification of monitoring data and safety evaluation method of tailings dam

Outlier Detection of Gravity Dam Deformation Monitoring Data Based on the Multiple Local Outlier Coefficient Method

Intelligent early warning of highway tunnel safety monitoring data based on Markov model

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