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Batch process fault detecting method based on ICA-KNN

A fault detection and matrix technology, applied in electrical testing/monitoring, testing/monitoring control systems, instruments, etc., can solve the problems of data scale making it difficult to apply FD-KNN, occupying storage space to record data, and consuming a lot of time. The effect of reducing computational complexity and high failure detection rate

Inactive Publication Date: 2016-07-06
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

However, the FD-KNN method has corresponding defects. For example, when the batch process data is expanded, the variable scale will increase rapidly, causing FD-KNN to consume a lot of time for the calculation of data information, and occupy a large amount of storage space to record data. Huge Data scale makes it difficult to apply FD-KNN

Method used

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  • Batch process fault detecting method based on ICA-KNN
  • Batch process fault detecting method based on ICA-KNN
  • Batch process fault detecting method based on ICA-KNN

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Embodiment

[0038] For the convenience of description, the relevant technical terms appearing in the specific implementation are explained first:

[0039]ICA (Independent Component Analysis) independent component analysis;

[0040] KNN (K-NearestNeighbor) K nearest neighbor;

[0041] FD-KNN (FaultDetectionbasedonK-NearestNeighbor) fault detection method based on K-nearest neighbor;

[0042] KICA (Kernel Independent Component Analysis) nuclear independent component analysis;

[0043] figure 1 It is a flowchart of the fault detection method for intermittent process based on ICA-KNN.

[0044] In this example, if figure 1 Shown, a kind of intermittent process fault detection method based on ICA-KNN of the present invention comprises the following steps:

[0045] S1. Data preprocessing

[0046] The three-dimensional sample matrix X(I×J×K) collected in the batch process is first expanded based on the number of batches to obtain the two-dimensional matrix X(I×KJ), so as to eliminate the in...

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Abstract

The invention discloses a batch process fault detecting method based on ICA-KNN. The method comprises the steps of: processing a training data set through applying the ICA; selecting fewer independent principal elements to replace original high dimensional data, and simultaneously extracting main characteristics of the original data; and applying a KNN method to get a corresponding statistic control limit for the fault detecting in an independent principal element space. In this way, the fault detecting rate is relatively high in a non-Gaussian and nonlinear batch producing process, and compared with KICA, the calculating complexity is reduced.

Description

technical field [0001] The invention belongs to the field of batch process technology, and more specifically relates to an ICA-KNN-based batch process fault detection method. Background technique [0002] Batch process, also known as batch process. Because of its flexible operation, it is widely used in the production of small batches and high value-added products. Nowadays, the batch process has become the main production method in industries such as fine chemical industry, biopharmaceutical and deep processing of agricultural products. The semiconductor batch production process has the characteristics of batch unequal length, process center drift, variable nonlinearity and multiple working conditions. In order to reduce the scrap rate in the semiconductor wafer production process, fault detection methods have become a key topic. [0003] Multivariate statistical analysis, such as principal component analysis (PCA) and partial least squares (PLS) and independent principal...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B23/02
CPCG05B23/0221G05B23/024
Inventor 何建章文邹见效凡时财张刚
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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