Fault Detection of Harmonic Drive Using Multiscale Convolutional Neural Network

Abstract

Harmonic drive is a key component in industry robot. Its complex design is highly sensitive to the manufacturing and assembly errors. Even a small error could cause excessive vibration jeopardizing the performance of the robot. Moreover, the vibration is dependent on the operation conditions. As a result, it is necessary to carry out a comprehensive test to detect possible manufacturing and assembling faults. This article presents an intelligent approach that can automatically identify different health conditions of harmonic drives. First, the acquired data from multiple acceleration sensors are cascaded in sequence after several preprocessing steps. Then, the multiscale convolutional neural network (MSCNN) architecture is used for disposing the inherent multiscale characteristics of the harmonic drive vibration signal, which can simultaneously perform multiscale feature extraction and classification. Finally, a large number of experiments were conducted on a real industrial robot vibration test platform to evaluate our approach. Based on the experiment results, the fault detection classification accuracy is 96.79%, which is higher than the other compared methods. This demonstrates that the presented method is effective for shop floor applications.