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

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...

AI Technical Summary

Problems solved by technology

[0006] The high-dimensional and nonlinear characteristics of complex industrial process data lead to defects such as large amount of calculation and long calculation time in the operation of its fault monitoring system.

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