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

A technology of sparse representation and process monitoring, applied in electrical testing/monitoring, etc., can solve problems such as weak method applicability, and achieve the effect of strong interpretability and wide application range

Inactive Publication Date: 2013-09-18
ZHEJIANG UNIV
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Problems solved by technology

Most of the above methods assume that the process variables satisfy the normal distribution assumption. Such

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

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Embodiment Construction

[0013] A multi-working condition process monitoring method based on sparse representation proposed by the present invention, its flow chart is as follows figure 1 shown, including the following steps:

[0014] 1) Use the multi-sensor data acquisition system to collect the data of each normal working condition in the process to form a dictionary (here represents the database) Among them, k represents the number of normal working conditions of the process, Indicates the data matrix (sub-dictionary) corresponding to the process condition i, and m is the number of process variables.

[0015] 2) For dictionaries normalized so that l of each column of data in 2 The norm is that the length of the column vector length is equal to 1, and the new normalized dictionary matrix is ​​obtained as

[0016] 3) The process is running online. The multi-sensor data acquisition system is also used to collect m process variable data. The online running data of the process obtained each t...

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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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Application Information

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IPC IPC(8): G05B23/02
Inventor 杨春节周哲文成林
Owner ZHEJIANG UNIV
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