针对掘进机截割振动信号故障特征不易提取和识别困难等问题，提出了一种精细复合多尺度模糊散布熵（refined composite multiscale fuzzy dispersion entropy, RCMFDE）与河马优化随机森林（hippo optimized random forest, HORF）的掘进机截割头故障诊断新方法。首先，利用RCMFDE全面表征掘进机截割头故障特征信息，构建故障特征数据集；其次，采用HORF对故障类型进行训练和测试，实现掘进机截割头的故障模式识别；最后，将所提方法运用在掘进机截割头实验数据分析中，并将其与现有的多尺度模糊熵、精细复合多尺度散布熵故障特征提取方法做比较。实验结果显示：RCMFDE在挖掘故障特征信息方面优于其他两种熵方法，而河马随机森林在故障分类方面优于极限学习机和支持向量机等分类器，所提故障识别模型可以更加精确地识别掘进机截割头的故障类型，且识别准确率达到100%。

To address the challenges of extracting and identifying fault features from roadheader cutting vibration signal, a new fault diagnosis method of roadheader cutting head based on the refine composite multi-scale fuzzy dispersion entropy(RCMFDE) and hippo optimized random forest(HORF) was proposed. Firstly, RCMFDE was used to comprehensively characterize the fault feature information of the roadheader cutting head, and the fault feature data set was constructed. Secondly, the fault type was trained and tested by the HORF to realize the fault pattern recognition of the cutting head of the roadheader. Finally, the proposed method was applied to the experimental data analysis of the cutting head of the roadheader, and compared with the existing multi-scale fuzzy entropy and fine-complex multi-scale spread entropy fault feature extraction methods. The results of the trial indicate that RCMFDE performs better than the other two entropy approaches in discovering defect features, and hippo random forest outperforms extreme learning machine and support vector machine in error recognition. The fault diagnosis method can more correctly recognize the error type of the cutting head of the roadheader, and the rate of accuracy of the recognition obtained 100%.