冷水机组作为建筑中的关键设备和主要能耗源，若其发生故障不仅会影响系统的正常运行，还会造成严重的能源浪费。为提升冷水机组系统运行的可靠性，构建了一种多策略改进蜣螂优化算法（improve dung beetle optimizer, IDBO）和混合核极限学习机（hybrid kernel extreme learning machine, HKELM）融合的故障诊断模型，用于实现冷水机组早期故障的精确诊断。该模型首先采用混合核函数提高核极限学习机（kernel extreme learning machine, KELM）的学习能力和泛化性，其次将Bernoulli映射、自适应惯性因子和Levy飞行融合动态权重系数策略用于改进蜣螂优化算法（dung beetle optimizer, DBO），以平衡DBO算法的全局探索性能。最后通过基准函数验证IDBO算法的有效性，利用IDBO算法对HKELM超参数进行优化，从而构建用于冷水机组早期故障诊断的数据驱动模型。通过相关训练仿真和实验验证，所提出的IDBO-HKELM模型对冷水机组的早期故障诊断准确率提高到99.71%，对比其他算法具有明显优势。

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.