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Sensor data verification method based on matrix singular values association rules mining

A technology of data verification and matrix singularity, which is applied in the directions of instruments, character and pattern recognition, electrical testing/monitoring, etc., can solve problems such as difficult selection of measurement points and inaccurate grasp of the correlation of measurement points, so as to achieve accurate measurement points and calculation The effect of high speed and improved reliability

Inactive Publication Date: 2009-07-08
SOUTHEAST UNIV
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Problems solved by technology

However, the traditional method of finding these measuring points uses qualitative methods such as analyzing the mathematical model of the equipment and directly observing the trend curve to determine the similar relationship between the measuring points. Disadvantages such as difficulty

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  • Sensor data verification method based on matrix singular values association rules mining
  • Sensor data verification method based on matrix singular values association rules mining
  • Sensor data verification method based on matrix singular values association rules mining

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Embodiment Construction

[0043] Firstly, the measurement points with similar association rules are mined through the singular value of the matrix, and then the modeling is carried out through these measurement points, including the residual generation model and the reconstruction model of each input line data. The implementation process is divided into two parts, which are described in detail as follows:

[0044] Part 1: Mining of Similar Association Rules Measurement Points

[0045] Mining measuring points with similar association rules through matrix singular values, the specific steps are as follows:

[0046] Step 1: Collect n measuring points that need to mine similar association rules and place them in the program list, where n is the number of measuring points;

[0047] Step 2: Follow the The method combines any pair of measuring points, collects the normal operation data of a sampling time interval Δt in a certain period of time, and forms an m×2 order matrix X, where m is the number of samp...

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Abstract

The invention relates to a method for checking sensor data mined based on an association rule of matrix singular values, which is realized by two parts, namely, the first part is measuring point mining of the similar association rule, while the second part is online checking of the sensor data; and the method comprises the following steps: modeling sought measuring point groups with similar association for least square support vector regression, and selecting all operating condition data by a training sample to cover all operating conditions; and for n measuring points, aggregately establishing (n+1) regression models comprising one residual error generating module of n inputs subtracting n outputs for monitoring operating data, and n No. K data reconstruction modules of (n-1) inputs subtracting a single output for reconstructing the operating data of each measuring point, wherein n is the number of sensors for checking the data on line. The invention provides a concept of fluctuation similarity among measuring points of running equipment in the field of uninterrupted industrial production, expands the concept of association rules to similar fluctuated association rules, is supplement and efficient expansion for the association rules in the field, and has actual significance.

Description

technical field [0001] The invention is a data mining method for finding combinations of measuring points with similar association rules by using matrix singular values, and is used for data verification and fault detection of thermal power plant sensors. Involved in the field of data mining and data verification. Background technique [0002] Data mining digs out useful information from a large amount of data, and it is a multidisciplinary interdisciplinary research field. Association rules are to mine and discover valuable associations in large amounts of data. It is an important topic in data mining and has been extensively studied by the industry in recent years. At present, association rule mining is widely used in business, medical insurance, finance, telecommunications, industrial production, etc., so the research on it is of great significance. There are still many problems in the practical application of association rules. For example, some other association relat...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B23/02G06K9/62
Inventor 邱凤翔司风琪徐治皋
Owner SOUTHEAST UNIV
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