Research on fault diagnosis technology of heat meter based on multi classifier fusion of pigeon swarm algorithm

Shuchun Yu; Jinjian Tao; Jun Liu; Yanshu Miao; Shuchun Yu; Jinjian Tao; Jun Liu; Yanshu Miao

doi:10.3934/mbe.2023272

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 4: 6312-6326. doi: 10.3934/mbe.2023272

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Research article

Research on fault diagnosis technology of heat meter based on multi classifier fusion of pigeon swarm algorithm

1.
China Higher Educational Key Laboratory for Measuring & Control Technology and Instrumentation of Heilongjiang Province, Harbin University of Science and Technology, Harbin 150080, China
2.
College of Civil and Architectural Engineering, Heilongjiang Institute of Technology, Harbin, Heilongjiang 150001, China
3.
School of Architecture, Harbin Institute of Technology, Harbin 150001, China
4.
Ministri Ind & Informat Technol, Key Lab Cold Reg Urban & Rural Human Settlement E, Harbin, Heilongjiang 150001, China

Received: 29 September 2022 Revised: 03 January 2023 Accepted: 08 January 2023 Published: 31 January 2023

In order to improve the availability of fault data, the fault data of heat meters had been classified, and balances all kinds of fault data according to interpolation algorithms to meet the needs of fault diagnosis algorithms. Based on the voting mechanism, an integrated model of multi classifier fusion is established, and the weight of each classifier is optimally configured through pigeon swarm algorithm. In the experiment, three kinds of integration models are obtained according to the voting mechanism and pigeon swarm algorithm. The three integrated models are used to diagnose the fault of the heat meter, and the three indicators of precision, recall and F1 Core have achieved satisfactory results.
- heat meter,
- fault diagnosis,
- integrated model,
- pigeon swarm algorithm
Citation: Shuchun Yu, Jinjian Tao, Jun Liu, Yanshu Miao. Research on fault diagnosis technology of heat meter based on multi classifier fusion of pigeon swarm algorithm[J]. Mathematical Biosciences and Engineering, 2023, 20(4): 6312-6326. doi: 10.3934/mbe.2023272

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Abstract

In order to improve the availability of fault data, the fault data of heat meters had been classified, and balances all kinds of fault data according to interpolation algorithms to meet the needs of fault diagnosis algorithms. Based on the voting mechanism, an integrated model of multi classifier fusion is established, and the weight of each classifier is optimally configured through pigeon swarm algorithm. In the experiment, three kinds of integration models are obtained according to the voting mechanism and pigeon swarm algorithm. The three integrated models are used to diagnose the fault of the heat meter, and the three indicators of precision, recall and F1 Core have achieved satisfactory results.

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