Equipment state variable selection method and equipment based on grey fuzzy analytic hierarchy process

Abstract

The invention relates to an equipment state variable selection method and equipment based on grey fuzzy analytic hierarchy process. The selection method comprises the following steps: acquiring multiple groups of candidate state variables; obtaining an evaluation index of each variable in each group of candidate state variables, and obtaining an evaluation value of the group of candidate state variables by using a grey fuzzy analytic hierarchy process; outputting a group of candidate state variables corresponding to the optimal evaluation value as final equipment state variables, wherein the evaluation values of the group of candidate state variables obtained by the grey fuzzy analytic hierarchy process are specifically as follows: constructing relative importance based on a fuzzy theory; calculating a gray evaluation weight matrix corresponding to the evaluation index of each variable based on gray class division and a gray evaluation theory; calculating to obtain a gray evaluation weight vector of each variable; and calculating and obtaining the evaluation value of the group of candidate state variables based on the relative importance among the variables and the gray evaluation weight vector. Compared with the prior art, the invention has the advantages of high reliability and the like.

Description

technical field [0001] The invention belongs to the technical field of industrial equipment state monitoring, and in particular relates to a method and equipment for selecting equipment state variables based on gray fuzzy hierarchical analysis. Background technique [0002] Various types of sensors are often embedded in manufacturing equipment, such as vibration sensors, acceleration sensors, temperature sensors, current sensors, etc., which can collect real-time vibration, temperature, current, etc., and directly or indirectly reflect changes in equipment status from different aspects monitoring data. At present, most of the related research in the field of equipment health management uses a single sensor signal as the analysis object for data mining. However, uncertain factors such as noise are likely to be mixed in the data collection, and different equipment states have different sensitivity to different sensor source signals. As a result, the accuracy of establishing a...

AI Technical Summary

Problems solved by technology

However, the judgment matrix in the AHP plays a decisive role in the results. The traditional AHP uses the 1-9 scale method to establish a pairwise comparison judgment matrix, and the specific scale is often determined according to expert experience, which is too subjective and only gives The weight of each indicator cannot determine the rationality of the selected indicator

In addition, the consistency test uses the maximum eigenvalue method to test the consistency of the judgment matrix. When the order of the matrix is ​​large, the process of solving the eigenvalues ​​is more complicated. After the consistency test is completed, if the consistency ratio does not meet the requirements, once the matrix fails Consistency check requires rebuilding the judgment matrix, which is a complicated process

Such as the Chinese patent application "A Method for Selection and Evaluation of Assembly Dimension Chain Paths Based on Analytic Hierarchy Process" (public number: CN110569520A), this method uses analytic hierarchy process for decision-making, but the subjective influence of the construction of the judgment matrix in the decision-making process is too large , moreover, a consistency check is required every time a decision is made, the process is complex

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