Complex industrial process fault prediction method based on RF noise reduction self-encoding information reconstruction and time convolution network

Pending Publication Date: 2021-11-12
BEIJING UNIV OF TECH
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Problems solved by technology

[0006] The high-dimensional and nonlinear characteristics of complex industrial process data lead to defects

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  • Complex industrial process fault prediction method based on RF noise reduction self-encoding information reconstruction and time convolution network
  • Complex industrial process fault prediction method based on RF noise reduction self-encoding information reconstruction and time convolution network
  • Complex industrial process fault prediction method based on RF noise reduction self-encoding information reconstruction and time convolution network

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Embodiment Construction

[0077] According to the actual chemical reaction process, American Eastman Chemical Company has developed an open and challenging chemical model simulation platform - Tennessee Eastman (TE) simulation platform. The TE chemical process is a prototype of an actual process flow. The whole process includes 5 main operating units, namely reactor, condenser, cycle compressor, gas-liquid separator and product desorption tower. The data generated by it have time-varying and strong coupling and nonlinear features, widely used for testing control and fault diagnosis models of complex industrial processes.

[0078] There are 41 measured variables and 12 manipulated variables in the TE process, and 21 fault types are set. The 12th manipulated variable XMV(12) remains unchanged throughout the process and is not used as input data. Select 52 variables [XMEAS(1), ..., XMEAS(41), XMV(1), ..., XMV(11)] as the original observation signal vector at a specific moment. In the simulation experime...

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Abstract

The invention discloses a new method for performing fault prediction on a complex industrial process. The method comprises two stages of fault state feature extraction and fault prediction. The fault state feature extraction comprises the following steps: firstly, screening out features related to faults from data of a complex industrial process by using a random forest algorithm; secondly, introducing a stack noise reduction self-encoding network for feature reconstruction, then constructing a square prediction error (SPE) statistic to serve as a fault state feature, and by utilizing a kernel density estimation method, determining a control limit; and finally, substituting new data into the model, calculating the statistical magnitude and judging whether the statistical magnitude is normal or not. The fault prediction comprises the following steps: forming a time sequence by using the SPEs, and then realizing trend prediction of the SPEs by using a prediction model of the SFTCN. According to the invention, the random forest algorithm is adopted to reduce the training cost of the stack noise reduction self-encoding network, the improved time convolution network is adopted to effectively extract the time sequence characteristics of the fault state, and the fault prediction precision is high.

Description

technical field [0001] The invention relates to the technical field of data-driven fault prediction, in particular to a fault prediction technology for continuous processes. The data-driven method of the present invention is a specific application in complex industrial process fault prediction. Background technique [0002] With the rapid development of information technology and automation technology, the integration and complexity of modern industrial systems are getting higher and higher. The interaction between various parts is also becoming more and more complex, leading to a gradual increase in the probability of system failure and functional failure, and once a failure occurs, the harmful impact will be great, and it will seriously cause the failure and paralysis of the entire system. Therefore, with the improvement of system reliability requirements, fault prediction technology has received great attention in both industry and academia. Fault prediction refers to p...

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Application Information

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IPC IPC(8): G06Q10/04G06N3/04G06N3/08G06K9/62
CPCG06Q10/04G06N3/049G06N3/08G06N3/048G06N3/045G06F18/24323
Inventor 高学金马东阳韩华云高慧慧
Owner BEIJING UNIV OF TECH
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