Gear Fault Diagnosis Under Variable Load Conditions Based on Acoustic Signals

The health condition of gears has been a topic of study in the past few decades due to the importance of gears for the transmission system. In recent years, some studies have used acoustic signals for gear diagnosis, which can overcome the limitation of vibration signals through noncontact measurement by air-couple. Although many acoustic-based diagnosis (ABD) methods have achieved good diagnosis performance of gear in stable working conditions, these methods suffer from effectiveness loss as the change of working load condition in the actual industry causes the domain shift problem. To overcome the above shortcoming, a domain-adversarial neural network (DANN) with a temporal attention mechanism (TAM) and a high dropout mechanism (HDM) is proposed in this article, which uses the acoustic signal of gears as the input of the model to detect gear health condition. First, the confrontation between the feature extractor and the discriminator in DANN is used to extract domain-invariant features for solving the domain shift problem. Then TAM is introduced into the feature extractor in DANN to refine domain invariant features for further enhancing domain adaptation ability to improve the diagnostic performance. Finally, HDM is utilized to erase the neurons of the input of the classifier with a random high probability to enhance the generalization ability of the model for further improving the classification performance. The experimental results show that the proposed method is effective to solve the domain shift problem of acoustic signals under variable load conditions.

Automated summary

This article proposes a neural network model for detecting the health condition of gears, which uses acoustic signals and addresses the domain shift problem through domain adversarial and attention mechanisms, thereby improving the diagnostic performance.