Course monitoring method based on non-gauss component extraction and support vector description

Abstract

The invention discloses a process monitoring method which is based on non-Gaussian component extraction and support vector description. The method comprises the following steps: read-in of training data and data to be diagnosed, data preprocessing, establishment of a principal component analysis model, particle swarm optimization algorithm, non-Gaussian projection calculation, support vector data description, residual analysis, principal component estimation, fault detection and the model updating. By the method, the non-Gaussian components can be automatically extracted from operating data of an industrial process, thus avoiding the disadvantage that the conventional statistical process monitoring method assumes that data is subject to normal distribution, and the non-Gaussian projection algorithm based on the particle swarm optimization algorithm ensures the maximization of the non-Gaussian properties of the extracted independent components, and avoids the problem that the independent component analysis method is easy to be involved in the locally optimal solution. Compared with the conventional statistical process monitoring method, the method can find abnormity in time, effectively reduce the rate of false alarm, and obtain better monitoring effect.

Description

technical field [0001] The invention relates to the field of industrial process fault diagnosis, in particular to a non-Gaussian statistical monitoring and fault detection method based on non-Gaussian component extraction and support vector description. Background technique [0002] With the rapid development of modern industry and science and technology, the modern process industry presents the characteristics of large scale, complex structure, strong coupling between production units, and large investment. At the same time, the possibility of malfunctions in the production process increases. Once this kind of system breaks down, it will not only cause huge loss of personnel and property, but also cause irreparable impact on the ecological environment. In order to improve the safety of industrial production process and control system, improve product quality and reduce production cost, process monitoring and fault diagnosis have become an indispensable part of enterprise i...

AI Technical Summary

Problems solved by technology

The FastICA algorithm is currently a commonly used algorithm for monitoring work based on ICA. Its shortcoming is that the separation result depends on the initial solution, and the global optimality of the solution cannot be guaranteed. In addition, it lacks an effective standard for selecting the number of pivots.

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