Gearbox fault diagnosis plays an irreplaceable role in ensuring the safe operation of rotating machinery equipment. However, many researches have only diagnosed single faults, and have not detected single faults from compound faults of gearbox. Therefore, in this paper, a framework based on random forest hybrid classifier (RFHC) is proposed for single fault detection, which not only identifies various fault types, but also separates the single fault from compound faults. Meanwhile, an improved Dempster–Shafer (IDS) information fusion method is developed to fuse the result obtained by the hybrid classifier. Extensive evaluations of the proposed methods on the QPZZ-II experimental platform datasets showed that the proposed framework detects the single faults from the compound faults effectively, which reduces the categorization complexity of a single classifier and improves the overall performance of the detection framework. Moreover, compared with the diagnosis result of a single sensor, IDS can achieve higher average fusion precision and improve the reliability of gearbox single fault detection.

变速箱故障诊断对于确保旋转机械设备的安全运行起着不可替代的作用。然而，许多研究仅诊断出单一故障，而没有从齿轮箱的复合故障中检测出单一故障。因此，本文提出了一种基于随机森林混合分类器（RFHC）的单故障检测框架，该框架不仅可以识别各种故障类型，而且可以将单故障与复合故障区分开。同时，开发了一种改进的Dempster-Shafer（IDS）信息融合方法以融合混合分类器获得的结果。在QPZZ-II实验平台数据集上对所提出方法的广泛评估表明，所提出的框架可以有效地检测出复合故障中的单个故障，这降低了单个分类器的分类复杂度，并提高了检测框架的整体性能。此外，与单传感器的诊断结果相比，IDS可以实现更高的平均融合精度，并提高了齿轮箱单故障检测的可靠性。