A method for online working condition process monitoring of plastic injection molding process

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...

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.

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