Process monitoring method based on simultaneous dimension reduction and dictionary learning

Abstract

The invention discloses a process monitoring method based on simultaneous dimension reduction and dictionary learning. The method comprises two steps of offline dictionary learning and online fault monitoring. At the offline dictionary learning stage, a simultaneous dimension reduction and dictionary learning method is put forward to carry out dictionary learning. At the online fault monitoring stage, three functions of fault detection, mode recognition and fault isolation are realized; fault detection is carried out on test data; if the test data are fault data, fault diagnosis is carried outto determine the fault position; and if the test data are not the fault data, mode recognition is carried out. According to the invention, problems of high-dimensional and multi-modal characteristicsof data are solved simultaneously by using projections and dictionaries learned by a simultaneous dimension reduction and dictionary learning method at the offline learning stage and a SPE statisticsamount is configured for the test data by using a projection matrix at the online fault monitoring stage, thereby reducing the calculation complexity of the online monitoring process and improving the real-time performance of online monitoring. Therefore, more spatial information of original data can be learned and reserved and the expression and identification capability of the dictionary is enhanced.

Description

technical field [0001] The invention relates to the field of process monitoring, in particular to a process monitoring method based on simultaneous dimensionality reduction and dictionary learning. Background technique [0002] As the degree of automation and integration of industrial process equipment increases, its structure becomes more and more complex, and the factors that affect the stable operation of equipment are also increasing, which makes the possibility of industrial process failures increasing. And the complexity of faults is getting higher and higher, so industrial process monitoring has aroused great interest in academia and industry. Due to the reduction in the cost of sensors and the rapid development of technology and the application of advanced computer technology, modern industrial systems have applied a large number of sensors to obtain richer process information and more accurate data, and the use of a large number of sensors to obtain process data is ...

AI Technical Summary

Problems solved by technology

[0004] In view of this, the present invention proposes a process monitoring method based on simultaneous dimensionality reduction and dictionary learning, which can learn and retain more spatial information of the original data, improve the representation and identification capabilities of the dictionary, and solve the problem of dictionary learning in the prior art. The best features of the original data cannot be effectively extracted, the representation and identification capabilities of the dictionary are low, and the dimensionality reduction process cannot guarantee that the projection matrix can extract the spatial structure of the original data.

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