Fault detection method and system based on global preserving unsupervised kernel extreme learning machine

Abstract

The invention provides a fault detection method and system based on a global preserving unsupervised kernel extreme learning machine. The fault detection method based on a global preserving unsupervised kernel extreme learning machine comprises an offline modeling step and an online monitoring step. The online monitoring step comprises the following sub-steps: normalizing test data, which is the condition data in a non-linear working process; calculating the kernel vector of the test data according to a kernel function, and centralizing the mean value of the kernel vector in a feature space toobtain a test kernel vector; extracting a low-dimension feature information matrix of the test data from the test kernel vector according to the global preserving unsupervised kernel extreme learningmachine, and calculating the monitoring statistics of the test data; and judging whether there is a fault in the non-linear industrial process according to whether the monitoring statistics of the test data exceeds the control limit, so as to achieve the purpose of real-time detection of a process fault.

Description

technical field [0001] The disclosure belongs to the technical field of nonlinear multivariable industrial process fault detection, and in particular relates to a fault detection method and system based on a global-preserving unsupervised kernel extreme learning machine. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] As modern industrial systems tend to be highly integrated and large-scale, the fault diagnosis of industrial processes has become a key technology to ensure the safe and stable operation of modern industrial systems. With the development of modern computer control technology, a wealth of process operation data is collected and stored in industrial processes. Therefore, data-driven fault detection and diagnosis technology has gradually become a research hotspot in the field of industrial process monitoring. Researchers have...

AI Technical Summary

Problems solved by technology

Although the unsupervised extreme learning machine technology has achieved certain application results in the field of nonlinear industrial process fault detection, the inventors found that its disadvantages are: (1) when the unsupervised extreme learning machine nonlinearly transforms the process data into a high-dimensional feature space The number of hidden layer nodes needs to be determined in advance, but the selection of the number of hidden layer nodes has always been a difficult problem

(2) When extracting the low-dimensional feature information of process data, the unsupervised extreme learning machine only maintains the local structure information of the data and ignores its global structure information. However, when ignoring the global structure information contained in the process data when extracting features, it will Affect the effect of fault detection

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