Electrolytic aluminum whole-process monitoring and fault diagnosis system based on multivariate statistic method

A fault diagnosis system and multivariate statistical technology, applied in the field of electrolytic aluminum process monitoring and fault diagnosis, to prevent pipeline leakage and prevent disease slots.

Active Publication Date: 2013-12-18
SHENYANG ALUMINIUM MAGNESIUM INSTITUTE
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Problems solved by technology

[0005] The present invention solves the problem of monitoring possible faults in the electrolytic aluminum process and ensuring timely alarm and troubleshooting of the faults. The purpose of the present invention is to provide a set of electrolytic aluminum full-process monitoring and fault diag

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  • Electrolytic aluminum whole-process monitoring and fault diagnosis system based on multivariate statistic method
  • Electrolytic aluminum whole-process monitoring and fault diagnosis system based on multivariate statistic method
  • Electrolytic aluminum whole-process monitoring and fault diagnosis system based on multivariate statistic method

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Embodiment Construction

[0089] Combined with the accompanying drawings Figure 1-Figure 9 The present invention is further described;

[0090] A whole-process monitoring and fault diagnosis system for electrolytic aluminum based on multivariate statistical methods. The system is divided into three layers. The bottom layer monitors key process parameters, the middle layer monitors equipment operation in the electrolytic aluminum process, and the top layer monitors the entire process. Comprehensive monitoring (including key process indicators);

[0091] The system monitors the top layer, middle layer, and bottom layer respectively, and analyzes the monitoring results of the three layers to determine the T value in each layer that reflects the variable amplitude in the PCA model. 2 Statistics, S reflecting the magnitude of variables in the ICA model 2 Whether the statistics and squared prediction error SPE statistics exceed the limit, so as to ensure the pertinence of monitoring and the accuracy of mo...

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Abstract

The invention discloses an electrolytic aluminum whole-process monitoring and fault diagnosis system based on a multivariate statistic method. The system comprises three layers, the bottom layer is used for monitoring key technological parameters, the middle layer is used for monitoring the device running in the electrolytic aluminum process, and the top layer is used for comprehensively monitoring the whole process. The system monitors the bottom layer, the middle layer and the top layer respectively, analyzes measured results comprehensively, and thus guarantees the monitoring pertinence and the accuracy of the monitored results. According to the invention, the separation and integrated monitoring is realized by adopting the concept of monitoring in layers, and further the monitoring pertinence and the accuracy of the monitored results are ensured; forecast before fault, alarming in fault and fault diagnosis and retrospect functions can be realized; the modular design concept is adopted for the off-line modeling and on-line monitoring, and each module performs the corresponding function; not only can the safety production and product quality be ensured, but also the consumption of raw materials and electricity energy can be reduced.

Description

technical field [0001] The invention belongs to the technical field of electrolytic aluminum process monitoring and fault diagnosis, in particular a whole-process monitoring and fault diagnosis of electrolytic aluminum based on a multivariate statistical method that can realize the functions of forecasting before a fault occurs, alarming when a fault occurs, fault diagnosis and fault tracing. diagnostic system. Background technique [0002] Modern science and technology are changing with each passing day, and the modern process industry is gradually developing in the direction of large-scale, complex, continuous and automated. People put forward higher requirements for high-quality, high-yield, low-consumption, and low-pollution process industries, and also put forward stricter requirements for safe production. In actual factory production, it is difficult for workers to know the status of the system from the large amount of collected data. They cannot know in time in the e...

Claims

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Application Information

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IPC IPC(8): C25C3/20
Inventor 刘志元
Owner SHENYANG ALUMINIUM MAGNESIUM INSTITUTE
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