Guo Dongdong, Zhang Yan, Chen Xiangqun, Peng Hao, Jiang Zongrui, Ma Haitao, Du Wenbo
Peking University School of Software and Microelectronics, 24 Jinyuan Road, Daxing Industrial District, Beijing, 102600, Beijing, China.
Beijing Benz Automotive Co., Ltd., 8 Boxing Road, Beijing Economic-Technologlcal Development Area, Beijing, 100176, Beijing, China.
Heliyon. 2024 Nov 13;10(22):e40115. doi: 10.1016/j.heliyon.2024.e40115. eCollection 2024 Nov 30.
The RV reducers are complex and sealed mechanical systems that are difficult to conduct fault diagnosis in advance. The previous research worked on the fault identification of RV reducer were mainly carried out on the test platforms instead of real complex working conditions. Most of faults were intentionally created in laboratory instead of real malfunction caused by factory daily operation. In the present paper, the actual failure mode of RV reducer for the industrial robots in factory is taken as the goal of fault diagnosis. The constant speed segment data extraction method is designed to overcome the difficulty of frequency domain analysis caused by non-uniform rotation in working conditions and ensure the quality and effectiveness of features extraction. Several machine learning classification models are selected regarding their inherent features. The proper DNN binary classification model shows the best performance that can meet the requirements of fault identification in industrial environment.
RV减速器是复杂的密封机械系统，难以提前进行故障诊断。以往关于RV减速器故障识别的研究主要在试验平台上进行，而非实际复杂工况。大多数故障是在实验室中人为制造的，而非工厂日常运行导致的实际故障。本文以工厂中工业机器人的RV减速器实际故障模式为故障诊断目标。设计了恒速段数据提取方法，以克服工况下转速不均匀导致的频域分析困难，确保特征提取的质量和有效性。根据其固有特性选择了几种机器学习分类模型。合适的深度神经网络（DNN）二分类模型表现出最佳性能，能够满足工业环境中故障识别的要求。
