Dynamic model-driven intelligent fault diagnosis method for rotary vector reducers

Abstract

The diagnosis of faults in rotary vector (RV) reducers using machine data-driven artificial intelligence (AI) models plays an important role, but it is difficult to obtain complete fault sample labeled data. Without labeled data, AI-based intelligent fault diagnosis models will fail. To solve the problem of data scarcity, a lumped parameter model of an RV reducer is developed to produce a sufficient training sample for AI models. First, a lumped parameter model of the healthy RV reducer is constructed and updated by the Pearson correlation coefficient (PCC) technique to obtain an agreeable dynamic model with a certain precision. Then, mathematical expressions of numerous fault modes with different fault severities are inserted into the model to calculate the fault samples. The simulated failure samples serve as training samples of AI-based intelligent models. Finally, CNN, VGG and ResNet are selected as the representatives of AI model, and then unknown fault samples are identified by applying data from real-time machinery. The experimental results suggest that the present method can be used to overcome the problem of insufficient fault samples in RV reducers.