A knn fault detection method for online upgrading master sample model

Abstract

A kNN fault detection method for online upgrading master sample model, relates to a batch process fault detection method, including using the data of the master sample space as a training set of modeling data, using the kNN fault detection method for modeling and fault detection, Find the k nearest neighbors of each sample in the main sample space, calculate the sum of the squares of the distances of the k nearest neighbors for each sample, arrange the sum of the squares of the k nearest neighbors of all samples in order to determine the threshold for fault detection; for the new A sample x to be detected, find the k nearest neighbors of x from the main sample space, calculate the square sum of the distances of the k nearest neighbors of the sample x and compare it with the threshold, if it is less than the threshold, the sample x is normal, otherwise the sample is faulty; through the selection of the master sample model, the data structure of each working condition can be optimized, and the accuracy of the fault detection model can be 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 exponentially...

AI Technical Summary

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 greatly increases the burden of data storage, but also cannot get the most accurate results

The principal component analysis method realizes the dimension reduction processing of the original data, but how to choose the most representative training set for modeling on a large amount of training data, there is no effective method to reduce the modeling space.

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