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Dynamic non-Gaussian structure monitoring data exception identification method

A technology of structural monitoring and recognition methods, applied in the testing of machines/structural components, electrical digital data processing, character and pattern recognition, etc.

Inactive Publication Date: 2017-06-27
DALIAN UNIV OF TECH +1
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Problems solved by technology

However, due to factors such as the nonlinearity of the structure and the complexity of the measurement noise, the structural monitoring data are often non-Gaussian; in addition, there are dynamic characteristics (i.e., autocorrelation) in the structural monitoring data

Method used

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  • Dynamic non-Gaussian structure monitoring data exception identification method
  • Dynamic non-Gaussian structure monitoring data exception identification method
  • Dynamic non-Gaussian structure monitoring data exception identification method

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Embodiment Construction

[0039] The specific implementation manners of the present invention will be further described below in conjunction with the accompanying drawings and technical solutions.

[0040] Select a two-span highway bridge model with a length of 5.4864m and a width of 1.8288m. A finite element model was established to simulate the structural response, and the responses of 12 measuring points were collected as monitoring data. A total of two data sets are generated: training data set and test data set; among them, the training data set is a normal monitoring data set, and a part of the test data set is used to simulate abnormal monitoring data; both data sets last for 80 seconds, and the sampling frequency is 256Hz. The basic idea of ​​the present invention is as figure 1 As shown, the specific implementation is as follows:

[0041] (1) Construct past observation vector x for each data point in the training data set p (t) and the current observation vector x c (t); for all x p (t) ...

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Abstract

The invention belongs to the field of civil engineering structure health monitoring, and provides a dynamic non-Gaussian structure monitoring data exception identification method. The method comprises the steps of firstly, defining past and current observation vectors for monitoring data, and performing pre-whitening on the past and current observation vectors; secondly, building a statistical correlation model for the whitened past and current observation vectors, and obtaining dynamic whitened data; thirdly, dividing the dynamic whitened data into a system-related part and a system-unrelated part, and performing independent component analysis modeling on the system-related part and the system-unrelated part; and finally, defining two statistical quantities, determining control limits of the two statistical quantities, and when the statistical quantities exceed the control limits, judging whether an exception exists in the monitoring data. The non-Gaussianity and the dynamic characteristic of the structure monitoring data are considered at the same time, so that the exception in the data can be effectively identified based on the defined statistical quantities.

Description

technical field [0001] The invention belongs to the field of health monitoring of civil engineering structures, and proposes a method for identifying abnormalities in dynamic non-Gaussian structure monitoring data. Background technique [0002] Under the combined action of long-term load, environmental erosion and fatigue effects, the degradation of service performance of civil engineering structures is inevitable. In-depth analysis of structural monitoring data can detect the abnormal state of the structure in time and provide accurate safety warnings, which is of great practical significance to ensure the safe operation of civil engineering structures. At present, the abnormal identification of structural monitoring data is mainly realized by statistical methods, which are generally divided into two categories: 1) Univariate control charts, such as Shewhart control charts, cumulative sum control charts, etc. Establish control charts for monitoring data to identify abnorma...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50
CPCG06F30/20G06F18/20G01M5/0008G06F17/18G06F2218/08G06F2218/12G06F18/2134G06F18/2135G06F18/295
Inventor 黄海宾伊廷华李宏男马树伟
Owner DALIAN UNIV OF TECH
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