Multi-stage process quality forecast method based on hybrid MPLS

A process quality, multi-stage technology, applied in program control, electrical program control, comprehensive factory control, etc., can solve problems such as length inequality, achieve accurate quality prediction, and achieve the effect of process monitoring

Inactive Publication Date: 2017-11-17
HUZHOU TEACHERS COLLEGE
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Problems solved by technology

For the problem of different lengths of the same sub-phase in multiple batches, the dynamic time warping (DTW) algorithm is applied to synchronize the equal-length trajectories according to the minimum similarity and the longest response duration.

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  • Multi-stage process quality forecast method based on hybrid MPLS
  • Multi-stage process quality forecast method based on hybrid MPLS
  • Multi-stage process quality forecast method based on hybrid MPLS

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Embodiment Construction

[0036]In the present invention, a Gaussian mixture model with better statistical distribution is introduced to identify and cluster the multi-sub-stage data sets of the industrial process, and MPLS models are respectively established in each sub-stage data set, and the models of each sub-stage are fused according to the Bayesian principle. Quality prediction, this method realizes more accurate quality prediction on the basis of multi-directional partial least squares. Partial least squares (PLS) is an important method in multivariate statistical analysis. PLS focuses on the relationship between multidimensional matrices X and Y to find the optimal low-dimensional feature interpretation direction. This optimization is based on the input space to In the sense of the predictive power of the output space. Multidirectional Partial Least Squares (MPLS) is an extension of PLS, which uses multiple batches of historical process data matrix X (I×J×K) and quality data matrix Y (I×M×K) to...

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Abstract

The invention relates to a multi-stage process quality forecast method based on hybrid MPLS. The method includes the following steps: firstly, identifying stages of each batch of acquired data by using a GMM model; , synchronizing different lengths of the same sub-stages of multiple batches of the acquired data to be tracks of equal lengths by using the Dynamic Time Warping (DTW) algorithm in accordance with the smallest similarity and the longest response lasting time; establishing a single MPLS model in a variable expansion manner in the synchronized data; then, based on the Fisher Discriminant Analysis (FDA) method, looking for the best projection vector among respective data sets and minimizing relativity among sub-stage data samples, and introducing the nuclear density method to estimate the probability density distribution of respective sub-stage data in the best projection vector so as to conduct switching of online monitoring stages; and eventually, using the bayes theory to combine MPLS models in respective stages for quality forecast.

Description

technical field [0001] The invention belongs to the technical field of automatic control, and relates to a multi-stage process quality prediction method based on hybrid MPLS. Background technique [0002] In the industrial process, due to the limitations of the process and testing technology, the quality index of the product is difficult to directly measure online and needs to be analyzed offline, resulting in a certain time lag in product quality information, making it difficult to obtain online feedback and control of product quality. However, many easy-to-measure process variables in industrial processes contain final quality information. By analyzing the relationship between process variables and product quality measurements, the production process can be modeled to realize online prediction of product quality. [0003] The traditional analytical model-based production process quality prediction requires accurate mathematics and production experience, which limits its pr...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B19/418
CPCY02P90/02G05B19/41885
Inventor 王培良叶晓丰杨泽宇
Owner HUZHOU TEACHERS COLLEGE
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