An Intelligent Compound Fault Diagnosis Method Using Convolutional Neural Network for Harmonic Drive

Abstract

As the key component of industrial robot, harmonic drive is unfortunately also the weakest part of the robot. Therefore, fault diagnosis is needed to find out what causes the problems and hence, find the remedies. Most of the existing mature machine learning approaches can obtain ideal results in the fault diagnosis of rotating machinery. However, the shortcoming of these methods is that the classifier only can output one label when the test sample is a composite fault signal, rather than multiple labels. Consequently, these traditional methods cannot simultaneously identify and output every label in the composite fault signal. To solve this problem, an intelligent method named Multi-Sensors Convolutional Neural Networks with Sigmoid function Classifier (MSCNN-SC) is proposed for compound fault identification. CNN is adopted to effectively obtain the representative features of the original signals. Binary Cross-Entropy (BCE) Loss and Sigmoid functions are then employed to calculate and output the probability of each label. The decision strategies using adjusted thresholds are designed to obtain the decoupling results of compound fault diagnosis. The proposed approach is validated by the multi-axises harmonic drives of the industrial robot. The experimental results demonstrate that the proposed approach can correctly identify the compound fault.