Performance prediction of industrial robot harmonic reducer via feature transfer and Gaussian process regression

This paper addresses the problem of identifying faults in the harmonic reducers of industrial robots by analysing their vibration signals. In order to solve the problem of obtaining fault data and rotation error from harmonic reducers in service, an accuracy performance prediction method based on transfer learning and Gaussian process regression (GPR) is proposed. The Euclidean distance between the spectral sequence of each component is proposed as the fitness index to optimise the transition bandwidth of the filter banks. The optimised empirical wavelet transform (OEWT) is used for signal decomposition to obtain sensitive frequency bands. A feature transfer method based on semi-supervised transfer component analysis (SSTCA) is proposed to achieve target domain feature transfer under missing data conditions. A prediction model based on GPR is established using the mapped features to predict the performance and accuracy of the harmonic reducer. The effectiveness of the proposed method is verified through model evaluation indicators and degradation experiments.