Static-dynamic collaborative awareness complex industrial process operation state evaluation method

A technology of collaborative perception and operating status, applied in the direction of program control, comprehensive factory control, comprehensive factory control, etc., can solve the problems of incomplete perception of working condition information and missed reporting

Active Publication Date: 2021-02-12
CHINA UNIV OF MINING & TECH
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Problems solved by technology

[0004] Aiming at the problems existing in the above-mentioned prior art, the present invention provides a static-dynamic collaborative sensing complex industrial process operation state evaluation method, which effectively solves the problem that the existing evaluation methods are not comprehensive in perception of working condition information, and there are omissions and false positives. The problem of reporting phenomena can effectively guarantee the quality of industrial products

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  • Static-dynamic collaborative awareness complex industrial process operation state evaluation method
  • Static-dynamic collaborative awareness complex industrial process operation state evaluation method
  • Static-dynamic collaborative awareness complex industrial process operation state evaluation method

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Embodiment Construction

[0102] Such as figure 1 and figure 2 As shown, the present invention provides a static-dynamic cooperative sensing complex industrial process operation state evaluation method, which specifically includes the following steps:

[0103] Step 1: Use the KPI-Driven SFA algorithm to mine static-dynamic characteristics collaborative perception information, and establish an offline evaluation model of the operating state;

[0104] S11: Collect the production data of the production process to obtain the input data matrix X∈R N×m and the output data matrix Y∈R N , where N is the number of samples, m is the number of process variables, R is the set of real numbers, R N×m Represents a real matrix of N×m dimensions;

[0105] S12: Perform zero mean and unit variance processing on each column of the input data matrix X, denoted as X a , and the difference between two consecutive input data points in each column is denoted as ΔX a ; The output data matrix Y is also standardized, denot...

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Abstract

The invention discloses a static-dynamic collaborative awareness complex industrial process operation state evaluation method. The method comprises the following steps that: a KPI-Driven SFA algorithmis used for conducting static-dynamic characteristic cooperative sensing information mining, so that an offline evaluation model of an operation state is established; a sliding window technology is introduced, the similarity between the score vector and first-order difference of online data and the score vector and first-order difference of each state level is calculated, and static and dynamic evaluation indexes are calculated according to the similarity; effective evaluation rules are formulated, and online identification of the process operation state is realized according to the static evaluation indexes; and online identification of a process operation state change trend is realized according to the dynamic evaluation indexes, and comprehensive evaluation of each state and a transition process is completed, aiming at a non-optimum variable, a data-driven fault diagnosis method based on sparse learning is used to reduce the influence of irrelevant variables, and the accurate position of the non-optimum variable is traced according to group contribution GWC. The method can effectively guarantee the quality of industrial products.

Description

technical field [0001] The invention belongs to the technical field of industrial production process operation state evaluation, and in particular relates to a complex industrial process operation state evaluation method based on static-dynamic cooperative sensing. Background technique [0002] Traditional process monitoring only focuses on the occurrence of abnormal conditions. Due to process disturbances and uncertainties, even under normal operating conditions, the process may deviate from the optimal operating point, resulting in non-optimal or even poor operating conditions. In particular, in typical process industries such as mining processing and metallurgy in my country, raw materials change frequently, operating environments are complex, working conditions fluctuate violently, equipment operating conditions are not good, and product quality and process parameters cannot be fully detected in real time, resulting in frequent non-optimal states in the production process...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B19/418
CPCG05B19/41875G05B2219/32368Y02P90/02
Inventor 褚菲莫双双尚超许杨
Owner CHINA UNIV OF MINING & TECH
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