A novel on-line approach for evaluating transmission errors in harmonic drives

The health condition monitoring of harmonic drives plays an important role in ensuring the rotational motion accuracy of robot or manipulator joints. Capitalizing on the periodic and repetitive nature of manipulator tasks, a novel on-line data-driven evaluation method is developed in this paper, which employs a One-Dimensional Convolutional Neural Network (1D-CNN) and Hidden Markov Model (HMM) to classify static transmission errors of harmonic drives based on the current and torque data of their driving motors. The 1D-CNN encodes multiple dimensional data including numerical features, Wavelet Packet Energy Entropy (WPEE) values, and marginal spectrums of the current and torque data. Then, based on these features, the HMMs under different transmission error levels are trained. In order to demonstrate this method, a test rig is developed, in which the load moves in a reciprocating motion as an inverted pendulum. The load is driven by the servo motor and the harmonic drive. The experimental results show that the proposed evaluation method can identify six distinct levels of static transmission errors in harmonic drives with an accuracy greater than 91% in this study. The presented research holds potential for the development of a Prognostics and Health Management (PHM) system for robots.