Convolutional self-encoding fault monitoring method based on batch imaging

A convolutional self-encoding and fault monitoring technology, which is applied in the direction of kernel methods, neural learning methods, instruments, etc., can solve the problem of less fault monitoring applications, reduce modeling workload, reduce false positives, and improve accuracy

Active Publication Date: 2020-03-27
BEIJING UNIV OF TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This method was initially applied to image recognition, and in recent years has been gradually extended to abnormal detection of images, videos, wafers, etc., but it is rarely used in fault monitoring of intermittent processes.

Method used

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  • Convolutional self-encoding fault monitoring method based on batch imaging
  • Convolutional self-encoding fault monitoring method based on batch imaging
  • Convolutional self-encoding fault monitoring method based on batch imaging

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Embodiment Construction

[0032] The Pensim penicillin fermentation simulation platform is a standard platform developed by Professor Cinar of the Illinois Institute of Technology and others for the evaluation of the effectiveness of fault monitoring in batch processes. In this experiment, a total of 10 process variables are collected. The variable names are shown in Table 1. The sampling interval is 1h. 50 normal batches are selected as training samples, and 2 faulty batches are used as test samples. Among them, the failure batch 1 is a step change of the ventilation rate with an amplitude of 1 at 200h, and the failure batch 2 is a ramp change of the stirring power at 200h with a slope of 0.003.

[0033] Table 1 Variables used to build the model

[0034]

[0035] Based on the above content, the present invention is applied to the above-mentioned fermentation process simulation platform, and the specific implementation steps are as follows:

[0036] A. Offline modeling phase:

[0037] 1): Collecti...

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Abstract

The invention discloses a convolution self-encoding fault monitoring method based on batch imaging, and belongs to the technical field of batch process fault monitoring. The method comprises two stepsof off-line modeling and on-line monitoring. The offline modeling step comprises the following steps: firstly, normalizing three-dimensional data of the intermittent process; then, taking the two-dimensional array of each batch as an image to be directly input into a convolutional auto-encoder (CAE) to carry out deep unsupervised feature learning; and finally, constructing statistics and corresponding control limits for the features learned by the CAE by utilizing a support vector machine. The online monitoring step includes: normalizing the collected data, and carrying out batch filling; inputting the normalized and filled batch graph into the trained CAE to learn features; and calculating an online statistic, and comparing the online statistic with an offline control limit. Compared with the prior art, the technical scheme provided by the invention avoids information loss caused by data expansion, does not need to divide stages to reduce modeling workload, deeply extracts change characteristics of process variables, and reduces false alarm and missing report rate of intermittent process monitoring.

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 batch imaging-based convolution self-encoding fault monitoring method. Background technique [0002] At present, the batch production process is developing towards refinement and intensification. It is very important to effectively monitor the whole production process, because it can not only ensure the production safety of the batch process, but also improve product quality and production efficiency, reduce energy consumption and pollute. [0003] The most commonly used methods in intermittent process fault monitoring research are multivariate statistical methods with multiway principal component analysis (MPCA) and multiway partial least squares (MPLS) as the core. By constructing T 2 (Hotelling-T 2 ) and SPE (squared prediction error) statistics, and compared with the statistical...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/08
CPCG06N3/084G06F18/214G06F18/2411G06N3/088G06N20/10G06N3/045G06F11/008
Inventor 王普张海利高学金高慧慧
Owner BEIJING UNIV OF TECH
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