Xu Qitong, Liu Chang, Yang Enshan, Wang Mengdi
Key Laboratory of Advanced Equipment Intelligent Manufacturing Technology of Yunnan Province, Kunming University of Science & Technology, Kunming 650500, China.
Faculty of Mechanical & Electrical Engineering, Kunming University of Science & Technology, Kunming 650500, China.
Sensors (Basel). 2022 Aug 26;22(17):6442. doi: 10.3390/s22176442.
In fault diagnosis research, compound faults are often regarded as an isolated fault mode, while the association between compound faults and single faults is ignored, resulting in the inability to make accurate and effective diagnoses of compound faults in the absence of compound fault training data. In an examination of the rotate vector (RV) reducer, a core component of industrial robots, this paper proposes a compound fault identification method that is based on an improved convolutional capsule network for compound fault diagnosis of RV reducers. First, one-dimensional convolutional neural networks are used as feature learners to deeply mine the feature information of a single fault from a one-dimensional time-domain signal. Then, a capsule network with a two-layer stack structure is designed and a dynamic routing algorithm is used to decouple and identify the single fault characteristics for compound faults to undertake the diagnosis of compound faults of RV reducers. The proposed method is verified on the RV reducer fault simulation experimental bench, the experimental results show that the method can not only diagnose a single fault, but it is also possible to diagnose the compound fault that is composed of two types of single faults through the learning of two types of single faults of the RV reducer when the training data of the compound faults of the RV reducer are missing. At the same time, the proposed method is used for compound fault diagnosis of bearings, and the experimental results confirm its applicability.
在故障诊断研究中，复合故障常被视为一种孤立的故障模式，而复合故障与单一故障之间的关联被忽视，导致在缺乏复合故障训练数据的情况下无法对复合故障进行准确有效的诊断。在对工业机器人的核心部件旋转矢量（RV）减速器进行研究时，本文提出了一种基于改进卷积胶囊网络的复合故障识别方法，用于RV减速器的复合故障诊断。首先，使用一维卷积神经网络作为特征学习器，从一维时域信号中深度挖掘单一故障的特征信息。然后，设计了一种具有两层堆叠结构的胶囊网络，并使用动态路由算法对复合故障的单一故障特征进行解耦和识别，以进行RV减速器复合故障的诊断。该方法在RV减速器故障模拟实验台上得到验证，实验结果表明，该方法不仅能够诊断单一故障，而且在RV减速器复合故障训练数据缺失的情况下，通过学习RV减速器的两种单一故障，还能够诊断由两种单一故障组成的复合故障。同时，将该方法用于轴承的复合故障诊断，实验结果证实了其适用性。
