Penicillin fermentation process fault prediction method based on Attention-LSTM of multivariate time series

A multivariate time series, penicillin fermentation technology, applied in neural learning methods, complex mathematical operations, biological neural network models, etc., can solve problems such as insufficient long-term memory ability, gradient disappearance, gradient explosion, etc., to improve the accuracy and accuracy of system prediction performance and accurate fault prediction

Active Publication Date: 2021-06-22
HEBEI UNIV OF TECH
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  • Application Information

AI Technical Summary

Problems solved by technology

However, in the face of long-term sequences, the cyclic neural network is prone to problems such as gradient disappearance, gradient explosion, and insufficient long-term memory capacity.

Method used

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  • Penicillin fermentation process fault prediction method based on Attention-LSTM of multivariate time series
  • Penicillin fermentation process fault prediction method based on Attention-LSTM of multivariate time series
  • Penicillin fermentation process fault prediction method based on Attention-LSTM of multivariate time series

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0095] 1) Use the penicillin simulation platform Pensim to simulate and generate 20 batches of normal fermentation process data with a sampling time of 400h and a sampling interval of 1h, as the control limit for predicting whether a failure occurs and SPE cl ; Collect 20 batches of normal fermentation process data with a sampling time of 400 h and a sampling interval of 1 h and 1 batch of fault data with a slope of 5% stirring power introduced at a sampling time of 320 h as sample data, of which 20 batches of normal fermentation process data are used as Training set, 1 batch of fault data as a test set;

[0096] 3) respectively for and SPE train Do correlation analysis with the on-line measurable variable in the penicillin fermentation process and adopt Pearson (Pearson) correlation analysis, analysis result is as shown in table 1:

[0097] Table 1

[0098]

[0099] The present invention selects the on-line measurable process variables with P0.2 as significantly rele...

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Abstract

The invention discloses a penicillin fermentation process fault prediction method based on Attention-LSTM (Long Short Term Memory) of a multivariate time series, which comprises the following steps: firstly, selecting a plurality of related process variables in a penicillin fermentation process through Pearson correlation analysis, then endowing implicit vectors at different moments of an input sequence of the LSTM with different weights through an Attention mechanism, the neural network prediction model is enabled to be more effective in processing long-time sequence input, and fault prediction modeling of the penicillin fermentation process is realized. According to the method, the fault prediction model combining the attention mechanism and the LSTM is adopted to perform fault prediction on the penicillin fermentation process, so that the problem that important time sequence information is easily ignored when the existing LSTM processes long sequence input is solved, and the fault prediction based on the LSTM is more accurate.

Description

technical field [0001] The invention belongs to the field of fault prediction modeling and application in industrial fermentation production process, and specifically relates to a multivariate time series-based Attention-LSTM fault prediction method for penicillin fermentation process. Background technique [0002] In the actual production process, the fault diagnosis of the system is usually only based on the current situation, but if the early prediction of the system fault can be made, the fault can be found and eliminated in time before the system fails, so that the fault can be reduced impact on the system. Therefore, the fault prediction of the system is of great significance to ensure the safety of the system. [0003] Time series (or called dynamic sequence) refers to the sequence in which the values ​​of the same statistical index are arranged in the order of their occurrence time. Time series analysis is a time-domain method for identifying modal parameters by us...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/27G06F17/16G06F17/15G06N3/04G06N3/08
CPCG06F30/27G06F17/16G06F17/15G06N3/08G06N3/048G06N3/044
Inventor 梁秀霞庞荣荣杨凡李万通郭鹭陈娇娇
Owner HEBEI UNIV OF TECH
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