针对目前对矿井提升机整个系统进行健康状态评估与预测的相关研究相对较少的问题，建立了矿井提升机健康状态评估指标体系和评语集，设计了矿井提升机健康状态评估与预测系统。针对矿井提升机各组成系统的监测数据无法充分利用、健康状态评估结果不能满足实际生产需求的问题，提出了一种提升机健康状态模糊综合评估方法：引入相对劣化度表征提升机不同类型指标的健康度，并利用健康度对矿井提升机的健康状态进行量化；采用模糊综合评估法计算矿井提升机的健康状态，使用指数标度代替1—9标度对层次分析法（AHP）进行改进，以降低计算复杂度；采用CRITIC客观赋权法，结合主客观权重计算各子系统和指标的综合权重；根据模糊综合评估计算过程和最大隶属原则，得到矿井提升机的健康状态评估结果和故障原因。在提升机健康状态评估结果基础上，利用哈里斯鹰优化（HHO）算法优化支持向量回归（SVR）模型的重要参数，构建HHO−SVR模型对矿井提升机的健康状态进行预测，提高健康预测结果的准确性。实验结果表明：模糊综合评估方法能够准确实现提升机健康状态评估；与粒子群优化支持向量回归（PSO−SVR）、遗传算法优化支持向量回归（GA−SVR）、灰狼算法优化支持向量回归（GWO−SVR）模型相比，HHO−SVR模型的预测结果更接近实际值，具有更好的预测效果。

In response to the relatively limited research on health evaluation and prediction of the entire system of mine hoists, a health evaluation index system and comment set for mine hoists have been established. The health evaluation and prediction system for mine hoists has been designed. A fuzzy comprehensive evaluation method for the health of mine hoists is proposed to address the issues of insufficient utilization of monitoring data from various components of mine hoists, and the inability of health evaluation results to meet actual production needs. The method introduces relative degradation degree to characterize the health of different types of indicators of the hoist. The method uses health degree to quantify the health of mine hoists. The fuzzy comprehensive evaluation method is used to calculate the health of mine hoists. The analytic hierarchy process (AHP) is improved by replacing the 1-9 scale with an exponential scale to reduce computational complexity. The method uses CRITIC objective weighting method and combines subjective and objective weights to calculate the comprehensive weights of each subsystem and indicator. Based on the fuzzy comprehensive evaluation calculation process and the maximum membership principle, the health evaluation results and fault causes of the mine hoist are obtained. On the basis of the health evaluation results of the mine hoist, the Harris hawks (HHO) algorithm is used to optimize the important parameters of the support vector regression (SVR) model. The HHO-SVR model is constructed to predict the health of the mine hoist, improving the accuracy of the health prediction results. The experimental results show that the fuzzy comprehensive evaluation method can accurately evaluate the health of the hoist. Compared with particle swarm optimization support vector regression (PSO-SVR), genetic algorithm optimization support vector regression (GA-SVR), and grey wolf algorithm optimization support vector regression (GWO-SVR) models, the prediction results of the HHO-SVR model are closer to the actual values and have better prediction performance.