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A fault detection method for automated industrial equipment based on multi attribute decision fusion in knowledge graph

Gan Wufang

Diagnostyka

2024

Vol. 25, No. 4

art. no. 2024407

Automated industrial equipment is an important production equipment in modern industry, but the occurrence of equipment failures may seriously affect production capacity. A method based on multi-attribute decision fusion was studied and designed for fault detection of automation industrial equipment. During the process, a mapping structure between the data layer and the pattern layer of the knowledge graph was designed. Knowledge extraction was performed on unstructured and semi-structured texts, and the fault knowledge graph was established through knowledge verification operations. Then, the fault alarm data was processed using Cypher query language, and the semantics were blurred using fuzzy set theory. Finally, the correctness of the fault chain was analyzed through attribute weights and attribute value matrices. Then it searched for the source fault node of the fault. The experimental results showed that the research method maintains an average accuracy of 0.8046 or above in the mean accuracy test when the number of traceability fault chains is 17-18. In the analysis of actual fault detection effectiveness, the research method focused on the fault detection time of the 8-station robotic arm swing plate robot when the number of fault nodes involved increased to 12, which was only 72ms. This indicated that the research method can effectively detect faults in automated industrial equipment and has more accurate detection accuracy.

Multi-attribute decision-making based on q-rung dual hesitant power dual Maclaurin symmetric mean operator and a new ranking method

Li Li, Wang Jun, Ji Chunliang

Archives of Control Sciences

2022

Vol. 32, No. 3

627--658

The ability of q-rung dual hesitant fuzzy sets (q-RDHFSs) in dealing with decision makers’ fuzzy evaluation information has received much attention. This main aim of this paper is to propose new aggregation operators of q-rung dual hesitant fuzzy elements and employ them in multi-attribute decision making (MADM). In order to do this, we first propose the power dual Maclaurin symmetric mean (PDMSM) operator by integrating the power geometric (PG) operator and the dual Maclaurin symmetric mean (DMSM). The PG operator can reduce or eliminate the negative influence of decision makers’ extreme evaluation values, making the final decision results more reasonable. The DMSM captures the interrelationship among multiple attributes. The PDMSM takes the advantages of both PG and DMSM and hence it is suitable and powerful to fuse decision information. Further, we extend the PDMSM operator to q-RDHFSs and propose q-rung dual hesitant fuzzy PDMSM operator and its weighted form. Properties of these operators are investigated. Afterwards, a new MADM method under q-RDHFSs is proposed on the basis on the new operators. Finally, the effectiveness of the new method is testified through numerical examples.