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Fermentation process fault monitoring method based on multiple contraction automatic encoders

A technology of automatic encoder and fermentation process, which is applied in the direction of instrumentation, program control, electrical test/monitoring, etc., and can solve problems such as limiting AE fault monitoring performance, poor robustness of hidden layer features, and noise interference

Inactive Publication Date: 2020-06-23
BEIJING UNIV OF TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, AE is a feature extraction method based on the global structure of data, and the hidden layer features are less robust and susceptible to noise interference.
These greatly limit the fault monitoring performance of AE

Method used

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  • Fermentation process fault monitoring method based on multiple contraction automatic encoders
  • Fermentation process fault monitoring method based on multiple contraction automatic encoders
  • Fermentation process fault monitoring method based on multiple contraction automatic encoders

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Experimental program
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Embodiment Construction

[0056] Penicillin is an important antibiotic with high efficiency, low toxicity and wide clinical application. Its production process is a typical dynamic, nonlinear, multi-stage batch production process. PenSim2.0, a penicillin simulation platform developed by the process monitoring and technology group of Illinois State Institute of Technology in the United States, provides a standard platform for the monitoring, fault diagnosis and control of penicillin intermittent production process. A series of simulations of the fermentation process of penicillin can be realized on this platform, and relevant research has shown the practicability and effectiveness of this simulation platform.

[0057] This experiment takes PenSim2.0 as the simulation research object, sets the sampling interval as 1h, and selects 10 process variables to monitor the process operation status, as shown in Table 1. 41 batches of normal data and 2 batches of fault data were simulated, each batch lasted 400 ho...

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Abstract

The invention discloses a new method for carrying out real-time fault monitoring on a penicillin fermentation process. The new method comprises two stages of ''offline modeling'' and ''online monitoring''. The ''offline modeling'' comprises the following steps: expanding historical three-dimensional data into a two-dimensional data matrix; carrying out related sub-block division on accumulated error data by using mutual information (MI); and modeling and monitoring each sub-space by using a contraction auto-encoder (CAE) on the basis of the related sub-block division. The ''online monitoring''comprises the following steps: processing newly collected data according to a model; calculating statistics of the newly collected data, comparing the statistics with a control limit to judge whethera fermentation process runs normally or not; and finally constructing comprehensive statistics to fuse monitoring results of different subspaces together, and carrying out comprehensive analysis. According to the method, the sub-blocks are constructed by using the accumulated errors and the mutual information so that system complexity is effectively reduced, and fault monitoring sensitivity is improved; and a block dividing monitoring model reflects more local information in the process, and faults are easier to monitor.

Description

technical field [0001] The invention relates to the field of data-driven fault diagnosis technology, in particular to a fault diagnosis technology for batch processes. The data-driven method of the present invention is a specific application in fault monitoring of a typical batch process—penicillin fermentation process. Background technique [0002] In recent decades, batch processes have attracted much attention because they can meet the demands of producing high value-added products. However, its mechanism is complicated, its operation complexity is high, and its product quality is easily affected by uncertain factors. As a typical batch process, the penicillin fermentation process has the characteristics of strong nonlinearity, dynamics, and mixed Gaussian distribution. In order to ensure the safety and stability of the fermentation process operating system, an effective process monitoring scheme is established to detect timely Anomalies are necessary. [0003] At pres...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B23/02
CPCG05B23/0243G05B2219/24065
Inventor 刘腾飞高学金
Owner BEIJING UNIV OF TECH
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