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Small-fault detection method and device based on multiple moving average

A technology of fault detection and moving average, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve the problems of poor fault detection effect and poor sensitivity of small faults, etc., to improve the detection effect and increase the robustness Stickiness, the effect of reducing the impact

Active Publication Date: 2014-05-07
TSINGHUA UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] However, such fault detection methods based on multivariate statistics are less sensitive to tiny faults
Such as the multivariate statistics SPE in PCA, T 2 Poor fault detection for tiny faults

Method used

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Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0051] figure 1 Shown is a flow chart of the micro-fault detection method based on multiple moving averages according to Embodiment 1 of the present invention.

[0052] Step S101 , collect sample data of process variables under normal working conditions, use principal component analysis (PCA) method to establish a principal component analysis model, and obtain a load matrix P.

[0053] Specifically, collect normal working condition data, assuming that the detected object contains m sensors, then x∈R m ; Each sensor has n independent samples, where n is the total number of samples. Then the data under normal working conditions can be collected and the following normal working condition measurement matrix X can be constructed 0 =[x 1 ,x 2 ,...,x n ] Τ ∈R n×m ;

[0054] Preprocess the normal working condition measurement matrix, and convert the normal working condition measurement matrix X 0 Subtract the corresponding variable mean from each column and divide by the corr...

Embodiment 2

[0111] This embodiment is the application of the micro-fault detection method based on multiple moving averages in the simulation model of the present invention. The measured value of the sensor in the selected industrial process is:

[0112] x 1 (k)=0.3723s 1 +0.6815s 2 +e 1

[0113] x 2 (k)=0.4890s 1 +0.2954s 2 +e 2 (18)

[0114] x 3 (k)=0.9842s 1 +0.1793s 2 +e 3

[0115] where x 1 ,x 2 ,x 3 is the measured value of the sensor; s 1 ,s 2 as the real state, and set s 1 =10,s 2 =12;e 1 ,e 2 ,e 3 Gaussian white noise with a standard deviation of 0.1, representing the measurement noise of the sensor.

[0116] Step S201 , collecting sample data of sensor measurement values ​​under normal working conditions, using the principal component analysis (PCA) method to establish a principal component analysis model, and obtaining a load matrix P.

[0117] Three sensors m=3, 5000 sets of normal measurement data are generated through this model, forming a matrix...

Embodiment 3

[0161] The present invention provides a small fault detection device based on multiple moving averages, such as image 3 A block diagram of the device is shown. Device 301 includes:

[0162] The modeling module 3011 is used to collect sample data of process variables under normal working conditions, establish a principal component analysis model by using the principal component analysis PCA method, and obtain the load matrix P;

[0163] Preferably, the modeling module 3011 can also include a preprocessing module, which is used to collect sample data of process variables under normal working conditions, and construct a normal working condition measurement matrix X 0 ; Perform standardized preprocessing on the normal working condition measurement matrix to obtain a normal measurement matrix X; based on the normal measurement matrix, use the principal component analysis PCA method to establish a principal component analysis model to obtain a load matrix P.

[0164] The first st...

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Abstract

The invention discloses a small-fault detection method and device based on multiple moving average. The method includes the steps that sample data under normal working conditions are collected, a PCA model is built through the PCA method, and a load matrix P is acquired; multivariate statistics SPE and T2 of a first sliding time window are acquired based on the sample data of process variables under normal working conditions of the first sliding time window at each sampling moment; first statistics characteristics of the SPE and T2 of the first sliding time window are extracted; multiple sliding average treatment is carried out in terms of first statistics characteristics of the SPE and T2 of a second sliding time window, and second statistics of the SPE and T2 are acquired; a fault judgment interval for small fault detection is determined, and fault detection rules are defined; sample data of process variables of a working site are collected, the second statistics characteristics of the multivariate statistics SPE and T2 are acquired in the working site according to the load matrix P, and whether small faults occur or not is judged according to the fault detection rules.

Description

technical field [0001] The invention relates to the field of fault detection, in particular to a small fault detection method and device based on multiple moving averages. Background technique [0002] In the process of modern industrial operation, the requirements for system safety and reliability are gradually increasing. Fault detection is a key technology to ensure the safe operation of the system and improve system reliability, and it is also a key step to improve product quality. As the complexity of the system gradually increases and the amount of component data continues to increase, the fault detection method based on multivariate statistics has been paid more and more attention. For example, the fault detection method based on principal component analysis (PCA) has been widely used, and its fault detection performance is often better than that of univariate fault detection, because PCA increases the consideration of the linear relationship between variables. The ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01D18/00G06F19/00
Inventor 周东华郭天序陈茂银
Owner TSINGHUA UNIV
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