APHformerNET: A Gear Fault Diagnosis Model Based on Adaptive Prototype Hashing Optimisation Algorithm

Abstract

Fault-diagnosis methods based on deep learning technology have been widely applied in gear fault diagnosis. Gearboxes often operate under complex and harsh conditions, which can lead to faults. Therefore, monitoring the condition of gearboxes and diagnosing faults are crucial for ensuring the reliability and safety of the system. In response, this paper proposes a gear fault diagnosis model based on the adaptive prototype hashing (APH) optimisation algorithm for diagnosing faults in rotating machinery. This method combines the advantages of adaptive prototype hashing with transformers to improve the accuracy of fault diagnosis. The model utilises an adaptive prototype selection mechanism to dynamically select the most representative samples as prototypes and employs the transformer model to extract feature representations of the input data. In classification tasks using two datasets, the model achieved an accuracy of 98.11% under normal conditions. In experiments with added white noise and a smaller sample size, the accuracies reached 96.81% and 86.41%, respectively. Additionally, we conducted ablation experiments with advanced transformer models, where the APHformer model incorporating the APH layer achieved fault diagnosis accuracies exceeding 97%, significantly outperforming other combinations. Furthermore, T-SNE visualisation results indicate that the method performs well in feature representation. This study provides important insights into the field of gear fault diagnosis based on deep learning and has potential practical application values.