Multi-working-condition process monitoring method based on sparse representation

Abstract

The invention discloses a multi-working-condition process monitoring method based on sparse representation and belongs to the technical field of industrial process monitoring and diagnosing. According to the method, process data are not required to obey normal distribution, and only normal operating data of a process in a certain working condition are supposed to be identical to historical data of the working condition in distribution. The method includes: firstly, building a dictionary according to historical data of each working condition; and secondly, computing sparse representation of on-line data in the dictionary, and then judging whether a process is abnormal or not according to the concentration ratio of presentation coefficients. In addition, the process can be identified to be in a certain single working condition or transition process currently according to normal data, and accordingly products are guaranteed to meet production requirements. The concept of sparse representation is used for multi-working-condition process monitoring; the method does not require the process data to obey normal distribution, thereby being wider in application range and higher in interpretability.

Description

technical field [0001] The invention belongs to the field of flow industry process monitoring and fault diagnosis, in particular to a multi-working-condition process monitoring method based on sparse representation. Background technique [0002] For process monitoring and fault diagnosis, traditional methods mostly use multivariable statistical process control (MSPC), in which principal component analysis (PCA) and partial least squares (Partial Least Squares, PLS) ) as representatives and other methods have been successfully applied in industrial process monitoring. The traditional MSPC method assumes that the process runs under a single operating condition, but in fact, due to product changes, capacity adjustments, etc., the process often switches frequently among multiple operating conditions. [0003] For the problem of multiple operating conditions, the traditional method either uses a single MSPC model to cover all operating conditions, or uses a multi-model method to...

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Problems solved by technology

Most of the above methods assume that the process variables satisfy the normal distribution assumption. Such assumptions do not necessarily meet the actual situation, which will lead to weak applicability of the method.

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