Fault isolation and identification method in nonlinear process based on denoising autoencoder

Abstract

The invention discloses a fault isolation and identification method in a nonlinear process based on a denoising self-encoder. Use the sensor to collect training data and test data, construct statistics in the denoising autoencoder residual space and hidden feature space; calculate the process monitoring statistics of the test data, when the test data process monitoring statistics exceed the control limit of the process monitoring statistics, Then there is a fault; the objective function is established by introducing a regularization term, and the fault amplitude is obtained based on the adaptive moment estimation solution, and the fault amplitude of each sensor is used to judge the fault of each sensor, so as to realize the isolation and identification of the fault. The invention can meet the speed and accuracy requirements of large-scale process fault diagnosis in industrial process, and provides reliable and effective technical support for industrial production process control.

Description

technical field [0001] The invention belongs to a fault data processing method in the field of fault isolation and identification in an industrial nonlinear process, in particular to a fault isolation and identification method in a nonlinear process based on a denoising autoencoder (DAE). Background technique [0002] The development of science and technology makes the modern industrial architecture more and more complex, and data-driven process monitoring methods can effectively ensure the safety and reliability of such systems, including principal component analysis (PCA), nuclear PCA, canonical correlation analysis, etc. Multivariate statistical analysis methods are mainly used for research. However, although traditional methods can realize fault detection, the development of industrial engineering increases the number of variables and samples, and traditional methods are limited by problems such as "smearing effect" in dealing with nonlinear industrial processes. [000...

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Problems solved by technology

[0005] The present invention introduces l 1 Constraints with sparse characteristics such as norms can be used to obtain sparse contribution graphs to solve the problems of "smearing effect" faced by traditional contribution graph methods, identify the main variables that cause faults, and achieve fault isolation, which is helpful to realize the large-scale process. Troubleshooting

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