WO2018149387A1 - 一种动态非高斯结构监测数据异常识别方法 - Google Patents

一种动态非高斯结构监测数据异常识别方法 Download PDF

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WO2018149387A1
WO2018149387A1 PCT/CN2018/076577 CN2018076577W WO2018149387A1 WO 2018149387 A1 WO2018149387 A1 WO 2018149387A1 CN 2018076577 W CN2018076577 W CN 2018076577W WO 2018149387 A1 WO2018149387 A1 WO 2018149387A1
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data
monitoring data
dynamic
gaussian
whiten
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French (fr)
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伊廷华
黄海宾
李宏男
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大连理工大学
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
    • G06F18/2134—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on separation criteria, e.g. independent component analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
    • G06F18/2135—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on approximation criteria, e.g. principal component analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/29—Graphical models, e.g. Bayesian networks
    • G06F18/295—Markov models or related models, e.g. semi-Markov models; Markov random fields; Networks embedding Markov models
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M5/00—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings
    • G01M5/0008—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings of bridges
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/08—Feature extraction
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/12—Classification; Matching

Definitions

  • the invention belongs to the field of civil engineering structural health monitoring, and proposes a dynamic non-Gaussian structure monitoring data anomaly identification method.
  • the anomaly identification of structural monitoring data is mainly realized by statistical methods. It is generally divided into two categories: 1) single variable control maps, such as Shewhart control charts, accumulation and control charts, etc. The monitoring data respectively establish control charts to identify anomalies in the monitoring data; 2) multivariate statistical analysis, such as principal component analysis, independent component analysis, etc., which uses a correlation between multiple measurement points to establish a statistical model. And define the corresponding statistics to identify the anomalies in the monitoring data.
  • the present invention aims to propose a dynamic non-Gaussian structure monitoring data modeling method, on the basis of which two statistics are defined for identifying anomalies in the data.
  • the technical solution is: first, define the past and current observation vectors for the monitoring data, and pre-whiten them; secondly, the past and current observations after the whitening

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  • Bioinformatics & Cheminformatics (AREA)
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  • Databases & Information Systems (AREA)
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  • Testing And Monitoring For Control Systems (AREA)
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Abstract

一种动态非高斯结构监测数据异常识别方法，属于土木工程结构健康监测领域。首先，对监测数据定义过去和当前观测向量，并对其进行预白化；其次，对白化后的过去和当前观测向量建立统计相关模型，得到动态白化数据；接着，将动态白化数据划分为系统相关和系统无关部分，并对其进行独立分量分析建模；最后，分别定义两个统计量并确定其控制限，当统计量超过控制限时判断监测数据中存在异常。由于同时考虑了结构监测数据的非高斯性和动态特性，基于此定义的统计量可有效识别数据中的异常。

Description

一种动态非高斯结构监测数据异常识别方法 技术领域
本发明属于土木工程结构健康监测领域，提出了一种动态非高斯结构监测数据异常识别方法。
背景技术
土木工程结构在长期荷载、环境侵蚀和疲劳效应等因素的共同作用下，其服役性能的退化不可避免。深入分析结构监测数据，可以及时发现结构的异常状态并提供准确的安全预警，对确保土木工程结构的安全运营具有重要的现实意义。目前，结构监测数据的异常识别主要通过统计方法实现，一般分为两大类：1)单变量控制图，如休哈特控制图、累积和控制图等，该类方法对每个测点的监测数据分别建立控制图，以识别监测数据中的异常；2)多变量统计分析，如主成分分析、独立分量分析等，该类方法利用多测点监测数据之间的相关性建立统计模型，并定义相应的统计量以识别监测数据中的异常。
由于结构变形的连续性，结构相邻测点的响应数据之间也具有相关性。实际工程应用中，能够考虑这种相关性的多变量统计分析方法更具优越性。然而，由于结构的非线性和测量噪声的复杂性等因素，结构监测数据往往呈现非高斯性；此外，结构监测数据中也存在动态特性(即自相关性)。若能在结构监测数据建模过程中同时考虑非高斯性和动态特性，则可提升多变量统计分析方法的异常识别能力，使其在工程应用中更具实用价值。
发明内容
本发明旨在提出一种动态非高斯结构监测数据建模方法，在此基础上定义两个统计量用于识别数据中的异常。其技术方案是：首先，对监测数据定义过去和当前观测向量，并对其进行预白化；其次，对白化后的过去和当前观测向

Claims (1)

  1. Figure PCTCN2018076577-appb-100001
PCT/CN2018/076577 2017-02-16 2018-02-12 一种动态非高斯结构监测数据异常识别方法 WO2018149387A1 (zh)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112560165A (zh) * 2020-06-11 2021-03-26 中车青岛四方机车车辆股份有限公司 一种城轨车辆及其客室车门故障诊断方法
CN114415609A (zh) * 2021-12-22 2022-04-29 华东理工大学 一种基于多子空间划分的动态过程精细化监测方法

Families Citing this family (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106897509B (zh) 2017-02-16 2020-06-16 大连理工大学 一种动态非高斯结构监测数据异常识别方法
CN109682561B (zh) * 2019-02-19 2020-06-16 大连理工大学 一种自动检测高速铁路桥梁自由振动响应以识别模态的方法
CN111242632A (zh) * 2020-01-07 2020-06-05 石化盈科信息技术有限责任公司 一种识别套现账户的方法、存储介质及电子设备
CN111474911B (zh) * 2020-04-28 2021-03-16 浙江浙能技术研究院有限公司 面向高端燃煤发电装备非平稳运行的高斯非高斯特征协同解析与监测方法
US11676071B2 (en) * 2020-06-30 2023-06-13 Oracle International Corporation Identifying and ranking anomalous measurements to identify faulty data sources in a multi-source environment
CN113722996B (zh) * 2021-08-31 2024-10-29 重庆大学 基于k近邻算法的桥梁损伤诊断方法
CN116484213A (zh) * 2022-12-13 2023-07-25 山东省交通规划设计院集团有限公司 基于投影重构与深度学习的公路结构健康预测方法及系统
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CN118195641B (zh) * 2024-05-17 2024-08-06 智联信通科技股份有限公司 一种基于区块链的智能制造领域生产全流程溯源方法

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102323049A (zh) * 2011-07-18 2012-01-18 福州大学 数据不完备下基于一致性数据替换的结构异常检测方法
CN103116804A (zh) * 2013-01-31 2013-05-22 苏州科技学院 一种模糊神经网络模型及深基坑变形智能预测方法
CN103234753A (zh) * 2013-04-11 2013-08-07 华北电力大学 基于高斯过程建模的风电机组轴系状态监测方法
CN106384300A (zh) * 2016-09-27 2017-02-08 山东建筑大学 基于大数据和模糊模型的建筑异常用能检测方法及系统
CN106897509A (zh) * 2017-02-16 2017-06-27 大连理工大学 一种动态非高斯结构监测数据异常识别方法

Family Cites Families (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6892163B1 (en) * 2002-03-08 2005-05-10 Intellectual Assets Llc Surveillance system and method having an adaptive sequential probability fault detection test
US7590513B2 (en) * 2006-01-30 2009-09-15 Nec Laboratories America, Inc. Automated modeling and tracking of transaction flow dynamics for fault detection in complex systems
US9843596B1 (en) * 2007-11-02 2017-12-12 ThetaRay Ltd. Anomaly detection in dynamically evolving data and systems
CN101403923A (zh) * 2008-10-31 2009-04-08 浙江大学 基于非高斯成分提取和支持向量描述的过程监控方法
US20140108324A1 (en) * 2012-10-12 2014-04-17 Nec Laboratories America, Inc. Data analytic engine towards the self-management of complex physical systems
US20140278303A1 (en) * 2013-03-15 2014-09-18 Wallace LARIMORE Method and system of dynamic model identification for monitoring and control of dynamic machines with variable structure or variable operation conditions
CN104392136B (zh) * 2014-11-28 2017-12-19 东南大学 一种面向高动态非高斯模型鲁棒测量的高精度数据融合方法
CN104656635B (zh) * 2014-12-31 2017-10-13 重庆科技学院 非高斯动态高含硫天然气净化过程异常检测与诊断方法

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102323049A (zh) * 2011-07-18 2012-01-18 福州大学 数据不完备下基于一致性数据替换的结构异常检测方法
CN103116804A (zh) * 2013-01-31 2013-05-22 苏州科技学院 一种模糊神经网络模型及深基坑变形智能预测方法
CN103234753A (zh) * 2013-04-11 2013-08-07 华北电力大学 基于高斯过程建模的风电机组轴系状态监测方法
CN106384300A (zh) * 2016-09-27 2017-02-08 山东建筑大学 基于大数据和模糊模型的建筑异常用能检测方法及系统
CN106897509A (zh) * 2017-02-16 2017-06-27 大连理工大学 一种动态非高斯结构监测数据异常识别方法

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112560165A (zh) * 2020-06-11 2021-03-26 中车青岛四方机车车辆股份有限公司 一种城轨车辆及其客室车门故障诊断方法
CN114415609A (zh) * 2021-12-22 2022-04-29 华东理工大学 一种基于多子空间划分的动态过程精细化监测方法

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