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Analysis and monitoring method for large-range non-stationary transient continuous process

A non-stationary, large-scale technology, applied in the direction of program control, total factory control, total factory control, etc.

Active Publication Date: 2020-03-13
ZHEJIANG UNIV
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Problems solved by technology

[0006] The purpose of the present invention is to provide an analysis and monitoring method for large-scale non-stationary transient continuous processes in view of the shortcomings of the existing monitoring technology for large-scale non-stationary transient continuous processes

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  • Analysis and monitoring method for large-range non-stationary transient continuous process

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Embodiment Construction

[0091] The present invention will be further described below in conjunction with the accompanying drawings and specific examples.

[0092] The 1000MW ultra-supercritical unit is a highly complex industrial process with time-varying, dynamic characteristics and non-stationarity. The coal mill is one of the important auxiliary machines of the ultra-supercritical unit of the thermal power plant. Its working principle is: the raw coal enters the rotating grinding bowl through the central coal drop pipe connected to the coal feeder. After the raw coal falls into the grinding bowl, it moves radially outward to the grinding bowl ring under the action of centrifugal force. A bed of coal is formed on the mill bowl. The coal passes under the grinding roller device that can rotate around the shaft. At this time, the grinding force generated by the spring-loaded device is applied to the coal through the rotating grinding roller, and the coal is ground into powder between the grinding bow...

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Abstract

The invention discloses an analysis and monitoring method for a large-range non-stationary transient continuous process. According to the method, a brand-new data reconstruction strategy is provided,and a non-stationary sequence in a time dimension is reconstructed into a stationary sequence in a condition dimension. On the basis, a large-range non-stationary transient continuous process is divided into a plurality of conditional modes by analyzing static characteristics of the process. According to the method, fine-grained process analysis is carried out on each conditional mode to comprehensively describe potential distribution, and after static and dynamic information of process operation is analyzed, a new Bayesian inference statistical magnitude is defined to carry out on-line monitoring. According to the method, understanding of specific process characteristics is facilitated, reliability and credibility of actual online monitoring of the large-range non-stationary transient continuous process are enhanced, change of the running state of the large-range non-stationary transient continuous process can be accurately judged, faults can be found in time, and therefore safety ofactual production is guaranteed.

Description

technical field [0001] The invention belongs to the field of statistical monitoring of large-scale non-stationary transient continuous processes, and in particular relates to a method that considers the different effects of static information and dynamic information on understanding process characteristics, the compromise between model complexity and model accuracy, and the conditional segment division results For the impact of subsequent monitoring performance, a large-scale non-stationary transient continuous process is automatically divided into different conditional segments and the process is monitored according to the division results of the conditional segments. Background technique [0002] In the past few decades, with more and more measured data, machine learning techniques, such as multivariate statistical analysis methods, support vector machines, neural networks, and Bayesian inference methods, have been widely used in industrial processes. Analysis and monitori...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B19/418
CPCG05B19/4185G05B2219/31088Y02P90/02
Inventor 赵春晖陈军豪
Owner ZHEJIANG UNIV
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