Weighted Entropy Minimization Based Deep Conditional Adversarial Diagnosis Approach Under Variable Working Conditions

Intelligent mechanical fault diagnosis is a crucial measure to ensure the safe operation of equipment. To address the issue of model collapse in domain adversarial training and the problem posed by different training samples having different transferability not considered in transfer tasks, this article proposes a weighted entropy minimization based deep conditional adversarial diagnosis approach of rotating machines under variable working conditions. First, the features of vibration signals in the source domain and target domain are extracted by a weight-sharing one-dimensional deep convolution neural network. The feature vectors and category prediction vectors are then fused by multilinear mapping to carry out adversarial training in domain adaptation. The entropy of the output of the domain discrimination model provides the index by which to measure the transferability of training samples. The transferability weights of samples are applied to the entropy minimization loss to eliminate the influence of these samples that are hard to transfer in adversarial domain adaptation. Experimental datasets under variable working conditions support the value of our approach.

Automated summary

This article proposes a weighted entropy minimization based deep conditional adversarial diagnosis approach for rotating machines under variable working conditions. Feature extraction, adversarial training, and transferability weight application are used to eliminate the influence of hard-to-transfer samples in domain adaptation. Experimental datasets support the effectiveness of this approach.