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Intermittence process fault diagnosis method based on sub-period MPCA-SVM

A fault diagnosis and sub-period technology, applied in program control, instrumentation, electrical test/monitoring, etc., can solve problems such as complex diagnosis process and reduced modeling

Active Publication Date: 2016-06-01
BEIJING UNIV OF TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

At the same time, the number of modeling can also be reduced, thus solving the problem of complex diagnosis process caused by frequent model updates

Method used

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  • Intermittence process fault diagnosis method based on sub-period MPCA-SVM
  • Intermittence process fault diagnosis method based on sub-period MPCA-SVM
  • Intermittence process fault diagnosis method based on sub-period MPCA-SVM

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Embodiment Construction

[0069] 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 monitoring and fault diagnosis of batch processes. This platform has become an internationally influential penicillin simulation platform.

[0070] The present invention takes this platform as the simulation research object, sets the reaction time of each batch of penicillin fermentation as 400h, and the sampling interval as 1 hour, and selects 10 process variables for simulation research, as shown in Table 1. At the same time, the platform can set three faults: 1. Air flow, 2. Stirring power, 3. Substrate flow acceleration rate. There are two types of fault disturbances: step disturbance and ramp disturbance. The amplitude of the two disturbances, the introduction time and termination time of the disturbance can be further set.

[0071] Table 1 Variables used to build the mo...

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Abstract

The invention discloses an intermittence process fault diagnosis method based on a sub-period MPCA-SVM, and relates to the field of fault diagnosis based on pattern recognition. The method comprises the steps: firstly carrying out the unfolding of three dimensional data of a fermentation process, and carrying out slicing in a time direction; secondly carrying out the rough dividing and fine dividing of a time period for the intermittence process through employing an MPCA; and finally building an MPCA monitoring model and an SVM diagnosis model in each sub-period. The online fault diagnosis comprises the steps: carrying out the processing of the collected data according to a model, calculating a statistic quantity, and comparing the statistic quantity with a control limit. The production is carried out normally if the limit is exceeded, or else, the data is substituted into the SVM diagnosis model of the corresponding time period for fault diagnosis. The method just carries out the filling of data in a time period when a fault happens, and reduces the impact on the accuracy of the SVM fault diagnosis from manual excessive filling of known data. Meanwhile, the method also reduces the number of models, and solves a problem that a diagnosis process is complex because of the frequent updating of the model.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis based on pattern recognition, in particular to an online fault diagnosis technology for batch processes. The pattern recognition-based method of the present invention is a specific application in the fault monitoring of a typical batch process—penicillin fermentation process. Background technique [0002] Due to the characteristics of specific functions, high added value, small batches, and multiple varieties of products, batch processes account for an increasing proportion in production, so fault diagnosis for batch processes is becoming more and more important. However, the batch process has the characteristics of dynamics, strong nonlinearity and time period characteristics, and the operation complexity is high and the product quality is easily affected by factors such as the environment, which makes the research on its fault diagnosis face greater challenges. [0003] For the fault di...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B23/02
CPCG05B23/0254G05B2219/24048
Inventor 高学金薛攀娜李娇
Owner BEIJING UNIV OF TECH