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On-line condition process monitoring method for plastic injection moulding process

A process monitoring and working condition technology, applied in the field of industrial monitoring and fault diagnosis, can solve the problem of difficulty in guaranteeing the rationality of the PCA model

Active Publication Date: 2016-05-11
HUAZHONG UNIV OF SCI & TECH
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In actual production, there is a nonlinear and strong coupling relationship between variables and target values, so the rationality of the PCA model is difficult to guarantee
[0004] For machine learning methods represented by neural networks and support vector machines, they are prone to overfitting when dealing with large-scale, high-dimensional data samples.

Method used

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  • On-line condition process monitoring method for plastic injection moulding process
  • On-line condition process monitoring method for plastic injection moulding process
  • On-line condition process monitoring method for plastic injection moulding process

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Embodiment Construction

[0044] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not constitute a conflict with each other.

[0045] The method of the present invention aims at the problems that the data dimension is too high and the process monitoring is difficult in the prior art, and proposes a brand-new online working condition monitoring method. Firstly, all process variables under normal working conditions are collected, thereby establishing a training sample set (also It can be called a database), and then use Dif...

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Abstract

The invention discloses an on-line condition process monitoring method for a plastic injection moulding process, and belongs to the industrial monitoring and fault diagnosis field. The on-line condition process monitoring method comprises the steps: S1 utilizing a sensor to collect the data under various conditions, and forming a training sample set X for modeling; S2 performing data pre processing and normalization to enable the mean value of the training sample set X to be 0 and enable the variance to be 1, and then obtaining a matrix X'; S3 according to the matrix X', applying Gaussian kernel function to calculate and obtain a distance matrix W; S4 standardizing the distance matrix W, obtaining a Markov matrix P (1), obtaining P(t) by making the P(1) to migrate t times, and performing spectral decomposition of the obtained characteristic matrix X' based on the P(t); S5 inputting the characteristic matrix X' and the condition Tq corresponding to various samples into an error back propagation neural network in pairs to receive training, and preserving the neural network model with the highest prediction accuracy as the model for monitoring; and S6 performing practical monitoring. The on-line condition process monitoring method successively realizes on-line monitoring of high dimension data.

Description

technical field [0001] The invention belongs to the field of industrial monitoring and fault diagnosis, and more specifically relates to an online working condition process monitoring method of plastic injection molding process based on diffusion mapping and error backpropagation neural network. Background technique [0002] With the continuous advancement of industrial automation and the increasing integration and complexity of system equipment, it is increasingly difficult to meet the needs of modern industry by relying on manual process monitoring and fault diagnosis. The widespread use of sensors enables the automation of process monitoring and fault diagnosis. [0003] At present, the mainstream method is to use the process data collected by sensors to establish monitoring models. According to whether the model is linear or not, it can be divided into linear model mainly based on Principal Component Analysis (PCA) and nonlinear model represented by neural network and s...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/08G06N3/04G06F17/30G06Q50/04
CPCY02P90/30G06N3/084G06F16/90G06N3/04G06Q50/04
Inventor 周华民张云乔海玉黄志高杨志明李德群付洋毛霆
Owner HUAZHONG UNIV OF SCI & TECH
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