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.