A single fault detection method of gearbox based on random forest hybrid classifier and improved Dempster-Shafer information fusion

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.