Industrial process data modeling method for online self-adaptive fine-tuning deep learning

An industrial process and deep learning technology, applied in the field of industrial process, can solve problems such as equipment aging, model prediction performance degradation, difficulty in coping with changes in production conditions, etc.

Pending Publication Date: 2020-10-13
CENT SOUTH UNIV
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[0006] The present invention provides an industrial process data modeling method for online self-adaptive fine-tuning and deep lea

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  • Industrial process data modeling method for online self-adaptive fine-tuning deep learning
  • Industrial process data modeling method for online self-adaptive fine-tuning deep learning
  • Industrial process data modeling method for online self-adaptive fine-tuning deep learning

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Embodiment Construction

[0049] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will describe in detail with reference to the drawings and specific embodiments.

[0050] The present invention aims at the problem that the existing deep belief network modeling and prediction process are carried out separately, and the off-line modeling method is difficult to cope with the problem of model prediction performance degradation caused by changes in production conditions, fluctuations in working conditions, adjustment of ingredients and equipment aging, etc., and provides a Online Adaptive Fine-Tuning Deep Learning Approach to Modeling Industrial Process Data.

[0051] Such as Figure 1 to Figure 2 As shown, the embodiment of the present invention provides an online adaptive fine-tuning deep learning industrial process data modeling method, including: Step 1, according to the sampling time sequence, obtain the process var...

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Abstract

The invention provides an industrial process data modeling method for online self-adaptive fine-tuning deep learning. The method comprises the following steps: 1, acquiring process variables related to quality in an industrial process according to a sampling time sequence, labeling the process variables by utilizing the quality variables, constructing a labeled sample set, preprocessing data according to data characteristics, and dividing the labeled sample set into a training sample set and a query sample set; 2, training L restricted Boltzmann machines offline layer by layer in an unsupervised pre-training mode by using the training sample set, and stacking the L pre-trained restricted Boltzmann machines in sequence to form a stacked network. According to the industrial process data modeling method for online adaptive fine-tuning deep learning, the samples are selected based on similarity measurement to perform online updating on the offline model, the exclusive online model is established for the query samples, the accurate quality prediction of the query samples is completed, and the performance and precision of online prediction are improved.

Description

technical field [0001] The invention relates to the technical field of industrial processes, in particular to an industrial process data modeling method for online self-adaptive fine-tuning deep learning. Background technique [0002] With the implementation of the "Made in China 2025" national strategy, my country's industrial production level has been greatly improved and developed. Whether it is traditional industries such as chemical industry, papermaking, electric power and medicine, or modern industries such as robotics and 3D printing, there are increasingly higher requirements for the monitoring, control and optimization of the production process. In order to ensure the normal operation of industrial production processes, optimize the utilization efficiency of resources and effectively alleviate the pressure of environmental pollution, various advanced monitoring, control and optimization technologies have been widely used. The development and implementation of thes...

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Application Information

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IPC IPC(8): G06N3/08G06N3/04G06F16/903
CPCG06N3/084G06F16/90335G06N3/045
Inventor 袁小锋顾永杰王雅琳王凯阳春华桂卫华
Owner CENT SOUTH UNIV
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