HMM‐TCN‐based health assessment and state prediction for robot mechanical axis

Abstract

Aiming at the problems of high manual cost, low efficiency, and low precision of the mechanical axis health management in industrial robot applications, this paper proposes a health assessment and state prediction algorithm based on hidden Markov model (HMM) and temporal convolutional networks (TCN). First, the MPdist similarity comparison algorithm is used to construct the mechanical axis health index. Then the hidden Markov model is trained with observable sensor data. After that, the temporal convolution neural network is used to predict state transition time iteratively, and the predicted results are decoded by HMM. The experimental results show that the HMM-TCN model can accurately assess the health state of the mechanical axis and predict the state transition in real-time. The prediction accuracy of this method reaches 87.5%, and the error interval locates in [−3,9] time steps. The accuracy, early/late prediction indicators are better than HMM-RNN, HMM-LSTM, and HMM-GRU.