kNN (k-nearest neighbor) fault detection method for online upgrading master sample model

A fault detection and master sample technology, applied in software testing/debugging, program loading/starting, program control devices, etc., can solve problems such as reducing modeling space, increasing data storage burden, and not being able to obtain the most accurate results, etc., to achieve Optimize data structure, improve data utilization, and reduce the effect of training sample sets

Inactive Publication Date: 2014-01-01
SHENYANG INSTITUTE OF CHEMICAL TECHNOLOGY
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Problems solved by technology

[0003] With the increasingly extensive and in-depth application of computers in industrial control, the data encountered in industrial process fault detection will increase exponentially, and the influence of noise information contained in it will also increase continuously. Using all known data modeling to carry out Fault detection not only great

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  • kNN (k-nearest neighbor) fault detection method for online upgrading master sample model

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[0018] The present invention will be described in detail below in conjunction with examples.

[0019] In the present invention, in a large amount of normal historical data, the main sample is extracted by analyzing statistical characteristics such as covariance, correlation coefficient, and sample variance among original data samples, so that the original data space is compressed, and the newly collected normal data is substituted into The main sample model enables the main sample space to be updated online. Then, the k-Nearest Neighbor (kNN) rule is used for batch process fault detection based on master sample modeling with online upgrade. This technology solves the problems of large data volume, strong repeatability, high noise interference, and low data utilization rate of batch process modeling samples. In order to accurately select the main sample with obvious characteristics, it is necessary to analyze and process the statistical characteristics of the original sample. ...

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Abstract

The invention discloses a kNN (k-nearest neighbor) fault detection method for an online upgrading master sample model and relates to a fault detection method for a batch process. The kNN fault detection method for the online upgrading master sample model comprises the following steps of performing modeling and fault detection by utilizing the kNN fault detection method by taking data of a master sample space as a training set of modeling data, finding out k nearest neighbors of each sample from the master sample space, calculating the quadratic sum of distances of the k nearest neighbors on each sample, permuting the quadratic sums of the k nearest neighbors of all samples in sequence to determine a threshold value of fault detection; finding out k nearest neighbors of a sample x from the master sample space for the new to-be-detected sample x, calculating the quadratic sum of the distances of the k nearest neighbors of the sample x and comparing the quadratic sum of the distances of the k nearest neighbors of the sample x with the threshold value, judging that the sample x is normal if the quadratic sum of the distances of the k nearest neighbors of the sample x is smaller than the threshold value, otherwise, judging that the sample x is faulted. By the selection of the master sample model, data structures of all working conditions are optimized, and the accuracy of a fault detection model is improved.

Description

technical field [0001] The invention relates to a batch process fault detection method, in particular to a kNN fault detection method for online upgrading of a main sample model. Background technique [0002] Batch process is an important chemical production process, which is widely used in the production of high-quality, high value-added products industries, such as: biopharmaceuticals, semiconductor manufacturing, agricultural chemistry, etc. Therefore, the detection and fault diagnosis of the batch process has always been a research hotspot at home and abroad. In batch process fault detection, the collected data often presents the characteristics of non-Gaussian, nonlinear, and multi-working conditions, which puts forward high requirements on the performance of fault detection methods. [0003] With the increasingly extensive and in-depth application of computers in industrial control, the data encountered in industrial process fault detection will increase exponenti...

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Application Information

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IPC IPC(8): G06F11/36G06F9/445
Inventor 陈海彬张晓丹李元
Owner SHENYANG INSTITUTE OF CHEMICAL TECHNOLOGY
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