Method for correcting gross error and random error of measurement data

A random error and measurement data technology, which is applied in the direction of electrical digital data processing, special data processing applications, and mitigation of unwanted effects, to achieve the effects of reducing negative effects, reducing missed judgments and misjudgments, and improving computing efficiency

Inactive Publication Date: 2007-08-01
ZHEJIANG UNIV
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  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Significant error correction and data coordination techniques that are closely related have also undergone great development, such as the widely used data coordination projection matrix method and zero degree matrix method, and the measurement data detection method of significant errors, node detection method and principal component analysis However, there are unavoidable cases of missing and misjudgments in the detection of significant errors, so how to use historical data to improve the detection rate of significant errors, provide manual intervention for significant errors that cannot be detected, and improve the computational efficiency of data coordination becomes a challenging proposition

Method used

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  • Method for correcting gross error and random error of measurement data
  • Method for correcting gross error and random error of measurement data
  • Method for correcting gross error and random error of measurement data

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0054] Taking a certain heat exchange network shown in Figure 2 as an example, there are four devices including a splitter SPL, a heat exchanger HX, a mixer MIX and a valve VAL, and 6 measurement points located at the input and output ends of the device. Table 1 lists the true value, measured value, standard deviation and reliability of the network measurement points.

[0055] Table 1

[0056] Measurement point number

[0057] Note: *--measurement data contains significant error

[0058] In the 1# measurement value, because the measurement point F 3 and F 4 The reliability of is higher than other measurement points, they are selected as independent variables, then calculate the P matrix as:

[0059] 1 1 0 1 0 1 ...

Embodiment 2

[0069] The steam system containing 11 nodes and 28 streams shown in accompanying drawing 3 is taken as an example to test the present invention, and the standard widely adopted in the world is introduced:

[0070]

[0071]

[0072] The effectiveness of significant error detection is evaluated. Table 3 shows the effect of the application and the comparison with the commonly used MIMT significant error detection method.

[0073] Table 3. Significant error detection effect

[0074] Number of simulations

[0075] It can be seen from Table 3 that the significant error detection rate of this method is higher than that of MIMT method, and the method of sharing correlation matrix makes data correction benefit at the same time, and improves the efficiency of data correction.

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Abstract

This invention discloses one method to correct measurement data clear error and random error, which comprises the following steps: using Bayes method to extracting measurement list reliability from historical data; then leading clear error test as base for test error; using error test to get information and to reduce correction computation volume. This invention uses meter historical operation data as reliable base to determine one batch of high reliable computation volume.

Description

technical field [0001] The invention relates to a method for correcting significant errors and random errors of measurement data. Background technique [0002] In chemical enterprises, measurement data contains unavoidable random errors and significant errors. Due to the existence of these errors, these measurement data often cannot meet the balance of materials and energy. Therefore, before using the production data to make decisions on the production and operation of the enterprise, it has become the consensus of many enterprise informatization builders to perform significant error detection and data coordination on the data. Random errors in process data are produced by various random factors, such as instrument errors and signal conversion, which are inevitable and satisfy the characteristics of random distribution. Significant errors are caused by instrument failure, unstable operation or process leakage, etc. The frequency of significant errors is much smaller than ra...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/00G06F19/00G01D3/028
Inventor 荣冈李笕列王旭冯毅萍苏宏业
Owner ZHEJIANG UNIV
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