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Chiller Fault Diagnosis Method Based on IDBO-HKELM
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Hong WANG1, 2, Pan CHU1, 2, Da-song GUAN3, *, Yang GUO1, 2, Zeng-rui TIAN1, 2, Ying-jie SHENG1, 2
Science Technology and Engineering | 2025, 25(22) : 9505 - 9513
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Science Technology and Engineering | 2025, 25(22): 9505-9513
• Papers·Architectural Science •
Chiller Fault Diagnosis Method Based on IDBO-HKELM
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Hong WANG1, 2, Pan CHU1, 2, Da-song GUAN3, *, Yang GUO1, 2, Zeng-rui TIAN1, 2, Ying-jie SHENG1, 2
Affiliations
  • 1 College of Building Environment Engineering, Zhengzhou University of Light Industry, Zhengzhou 450000, China
  • 2 Henan Engineering Research Center of Intelligent Buildings and Human Settlements, Zhengzhou 450000, China
  • 3 China Construction Technology Group Ltd., Beijing 100013, China
Published: 2025-08-08 doi: 10.12404/j.issn.1671-1815.2408240
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As a key equipment and a major source of energy consumption in a building, chiller plant, if it fails, it will not only affect the normal operation of the system, but also cause serious energy waste. In order to improve the reliability of chiller system operation. A multi-strategy IDBO(improved dung beetle optimization algorithm) combined with a HKELM(hybrid kernel extreme learning machine) fusion fault diagnosis model was constructed to achieve accurate diagnosis of early faults in chiller systems. The model firstly employs hybrid kernel functions to improve the learning ability and generalization of KELM(kernel-extreme learning machine). Secondly, Bernoulli mapping, adaptive inertia factor, and Levy flight fusion dynamic weight coefficients strategies were used to improve the DBO(dung beetle optimization) algorithm in order to balance the global exploration performance of the DBO algorithm. Finally, the effectiveness of the IDBO algorithm was verified by benchmark functions, and the HKELM hyperparameters are optimized using the IDBO algorithm to construct a data-driven model for early fault diagnosis of chiller units. Through relevant training simulations and experimental validation, the accuracy of the proposed IDBO-HKELM model for early fault diagnosis of chillers is improved to 99.71%, which is an obvious advantage over other algorithms.

chiller  /  swarm algorithm  /  HKELM(hybrid kernel extreme learning machine)  /  fault diagnosis  /  IDBO(improved dung beetle optimization) algorithm
Hong WANG, Pan CHU, Da-song GUAN, Yang GUO, Zeng-rui TIAN, Ying-jie SHENG. Chiller Fault Diagnosis Method Based on IDBO-HKELM[J]. Science Technology and Engineering, 2025 , 25 (22) : 9505 -9513 . DOI: 10.12404/j.issn.1671-1815.2408240
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Year 2025 volume 25 Issue 22
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Article Info
doi: 10.12404/j.issn.1671-1815.2408240
  • Receive Date：2024-11-05
  • Online Date：2026-02-11
  • Published：2025-08-08
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History
  • Received：2024-11-05
  • Revised：2025-05-15
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Affiliations
    1 College of Building Environment Engineering, Zhengzhou University of Light Industry, Zhengzhou 450000, China
    2 Henan Engineering Research Center of Intelligent Buildings and Human Settlements, Zhengzhou 450000, China
    3 China Construction Technology Group Ltd., Beijing 100013, China
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多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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