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A Fault Monitoring Method for MKECA Fermentation Process Based on Extended Kernel Entropy Load Matrix

A load matrix and fermentation process technology, applied in the field of fault monitoring, can solve the problems of fault alarm time lag, large leakage alarm and false alarm, false alarm, etc., to improve the accuracy of the model, reduce the false alarm rate and the false alarm rate, The effect of failure monitoring time advance

Active Publication Date: 2022-03-15
BEIJING UNIV OF TECH
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Problems solved by technology

Most of the above methods obtain the load matrix through dimension reduction processing, and then use the variation direction or variation amplitude of the feature matrix to model, ignoring the existence of unknown noise in the actual production process, resulting in low accuracy and sensitivity of the built model, resulting in a large number of Missed and false alarms
Therefore, fully considering the existence of a large number of unknown noises and singular values ​​in actual production, the traditional method of only relying on the load matrix to establish a monitoring model has certain limitations, resulting in a large number of false alarms, missed alarms, and even failures during online real-time monitoring. There is a certain lag in the alarm time

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  • A Fault Monitoring Method for MKECA Fermentation Process Based on Extended Kernel Entropy Load Matrix
  • A Fault Monitoring Method for MKECA Fermentation Process Based on Extended Kernel Entropy Load Matrix
  • A Fault Monitoring Method for MKECA Fermentation Process Based on Extended Kernel Entropy Load Matrix

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Embodiment Construction

[0043] Penicillin (Penicillin, or transliteration Penicillin) is a widely used antibiotic, and its preparation process is a typical non-linear, multi-stage batch production process. PenSim2.0, a penicillin simulation platform developed by the Cinar team at Illinois State Institute of Technology in the United States, includes controlled variables, manipulated variables, and input and output variables, providing a standard platform for real-time monitoring of the penicillin production process.

[0044]In this experiment, the PenSim2.0 simulation platform was used to simulate the fermentation process of penicillin as the data source. The sampling interval was 1 h, and 10 process variables were selected, as shown in Table 1. A total of 32 batches of data were generated in the simulation, 30 batches of normal data were used to build the model, and the other 2 batches of fault data were used for testing to verify the effectiveness of the method. The types, amplitudes, start and end ...

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Abstract

The invention relates to a fault monitoring method of MKECA fermentation process based on an extended kernel entropy load matrix, which belongs to the technical field of fault monitoring. Including "offline modeling" and "online monitoring". "Offline modeling" step: firstly, the collected 3D data is subjected to dimensionality reduction and standardization processing, and the principal component information of the data is analyzed by using the nuclear entropy component; then the time is extended to the nuclear entropy load matrix to generate the nuclear entropy expansion load matrix, and Calculate the similarity between the kernel entropy expansion load matrices; finally, use the fuzzy-C-means to divide it into stages, establish a KECA monitoring model, and calculate statistics and corresponding control limits. "Online monitoring" step: standardize the collected data, calculate statistics and control limits; compare with the offline control limits, if the limit is not exceeded, the production process is normal, otherwise, the production process is faulty. The invention solves the problem of misclassification of jumping points, makes the divided stages more accurate, and reduces false positive and false negative rates.

Description

technical field [0001] The invention belongs to the technical field of fault monitoring, and relates to a data-driven intermittent process online fault monitoring technology, in particular to a method for stage division of a multi-stage intermittent process. Background technique [0002] In recent years, the batch production method is gradually surpassing the continuous production method to become the mainstream of the market, especially in the fields of medicine and biopharmaceuticals. The multi-stage characteristic is an inherent characteristic of the batch production process. There are different key process variables and control objectives in different stages. It is difficult to obtain satisfactory results by directly using the existing multivariate statistical process monitoring (Multivariate Statistical Process Monitoring, MSPM) method. A very typical phenomenon is that after the current model continues to run for a period of time, there will be a large number of false ...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/06G06F17/16G06Q50/04
CPCG06F17/16G06Q10/0639G06Q50/04Y02P90/30
Inventor 高学金杨彦霞王普
Owner BEIJING UNIV OF TECH
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