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Industrial system working condition monitoring method and system based on static and dynamic conjoint analysis

A technology of joint analysis and working conditions, applied in neural learning methods, character and pattern recognition, instruments, etc., can solve problems such as inapplicability, and achieve the effect of improving accuracy and enhancing robustness

Active Publication Date: 2021-06-25
CENT SOUTH UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Dobos et al. proposed a dynamic PCA-based multivariate process condition division method, but it needs to set the number of conditions and the value of the maximum segmentation error, which is not applicable in practice

Method used

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  • Industrial system working condition monitoring method and system based on static and dynamic conjoint analysis
  • Industrial system working condition monitoring method and system based on static and dynamic conjoint analysis
  • Industrial system working condition monitoring method and system based on static and dynamic conjoint analysis

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Embodiment 1

[0043] As the industrial system is getting more complex, single from static or dynamic feature does not accurately distinguish the correct working condition, only the inclusion of the two will be more realistic to change condition changes. On the other hand, with the continuous operation of the industrial process, there will be many new unknown working information. Traditional division methods may not be updated because the model cannot be updated, and the unknown works may get an error division result, which in turn affects subsequent monitoring and control.

[0044] Inspired by the above practical industrial process, this patent proposes an incremental working condition division method based on static and dynamic combined analysis. First, the method of the present invention consider static features and dynamic features in the industrial process, and can effectively grasp the characteristics of the industrial process. And according to the changes in each type of characteristics, ...

Embodiment 2

[0101] This embodiment provides an industrial system working condition monitoring system based on static and dynamic combined analysis, including feature extraction modules, SOM network training modules, control limit computing modules, and working conditions change time point judgment modules;

[0102] The feature extraction module is used to obtain an industrial process data sequence of a stable working condition i = a prior to the current industrial process, and the industrial process data of each sampling point is used as one training data sample, using a slow feature analysis method to extract training data. Sample static feature vector S and dynamic feature vector

[0103] The SOM network training module is used to use a static feature vector S using all training data samples, training 1 SOM network to obtain a static SOM network corresponding to the stable working case i = a, add it to the static SOM network group SOM sta Remember the static SOM network group SOM sta Stati...

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Abstract

The invention discloses an industrial system working condition monitoring method and system based on static and dynamic conjoint analysis, and the method comprises the steps: extracting the static and dynamic features of a sample through a slow feature analysis method for a segment of industrial process data sequence under a stable working condition; respectively training initial static and dynamic SOM networks by using the static and dynamic characteristics; calculating static and dynamic control limits of the current working condition by using the static and dynamic SOM network weights and an input calculation sample quantization error; acquiring industrial process data on line, extracting static and dynamic characteristics of the industrial process data, respectively calculating quantization errors with a static / dynamic SOM network, comparing with static and dynamic characteristic judgment control limits, judging whether working conditions at the current time point of the industrial process are changed or not, if so, training a new static and dynamic SOM network by using new data, and if not, using the sample to update the existing SOM network weight. According to the method, the working condition change time point of the industrial process can be more truly and accurately judged.

Description

Technical field [0001] The present invention belongs to the technical field of working conditions classification, and specific relates to an industrial system working condition monitoring method and system based on static and dynamic combined analysis. Background technique [0002] Today, industrial field equipment is increasingly complex, affected by internal mechanisms and external environments, which will be in different working modes during operation. Under different conditions, the corresponding monitoring indicators and control strategies will be distinct. For example, for the motor system, when the load changes, its normal operating current will inevitably change, which requires the monitoring model and the controller to switch between the working mode according to the change of the motor conditions. If the corresponding working condition cannot be clearly clarified, the monitoring model will have a phenomenon and missing, and the controller cannot switch the control strat...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/04G06N3/08G06F18/213G06F18/214
Inventor 黄科科韦可阳春华李繁飙李勇刚朱红求
Owner CENT SOUTH UNIV