Dynamic non-gaussian process monitoring method based on dynamic latent independent variable

Abstract

The invention discloses a dynamic non-gaussian process monitoring method based on a dynamic latent independent variable. The dynamic non-gaussian process monitoring method aims to combine the advantages of a dynamic latent variable model capable of processing dynamic data and an independent component analysis model capable of processing non-gaussian data. Specifically, the dynamic non-gaussian process monitoring method comprises the steps: firstly, a dynamic latent variable algorithm is utilized to extract auto-correlation dynamic characteristic components and cross-correlation static characteristic components; secondly, after the characteristic components are whitened, the combined whitening characteristic components are utilized as initial independent components to obtain a dynamic latent independent variable model in an iteration mode; and finally, dynamic non-gaussian process monitoring is carried out based on the dynamic latent independent variable. It can be said that the methodutilizes the capacity, for separately extracting the dynamic components and the static components, of the dynamic latent variable algorithm, then an independent component analysis algorithm which canextract the non-gaussian characteristic components is further combined, and thus the dynamic non-gaussian process monitoring method is feasible.

Description

technical field [0001] The invention relates to a data-driven process monitoring method, in particular to a dynamic non-Gaussian process monitoring method based on dynamic latent independent variables. Background technique [0002] Due to the extensive use of advanced sensors and computer technologies in modern industrial processes, production process objects can store and measure massive amounts of data offline and online. In the context of industrial "big data", data-driven process monitoring has been favored by many scholars in recent years. In fact, both academia and industry have invested a lot of manpower and material resources in the research of process monitoring methods with fault detection and diagnosis as the core task. In the field of data-driven process monitoring research, various machine learning algorithms, such as multivariate statistical analysis, manifold learning, and support vector machines, have been applied to process monitoring. Among them, the rese...

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Problems solved by technology

Due to the unavoidable serial autocorrelation of sampling data, the monitoring of dynamic non-Gaussian processes needs further research

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