井冈山大学学报（自然科学版）2025，Vol.46Issue(6)：95-102,8.DOI:10.3969/j.issn.1674-8085.2025.06.010
噪声干扰下基于多头注意力机制的圆锥滚子轴承故障诊断
Multi-attention mechanism based fault diagnosis of tapered roller bearings under noise interference
摘要
Abstract
In order to study the problem of fault diagnosis of tapered roller bearings under noise interference,a method based on multihead attention mechanism long short-term memory network(MHA-LSTM)is proposed.This method aims to extract the useful features from complex operating environments for the accurate identification of bearing fault conditions.By collecting vibration signals during the operation of tapered roller bearings,a noise interference model is constructed to simulate the most realistic working conditions.The noisy signals are directly used as inputs to the MHA-LSTM model for the feature extraction and classification.The experimental results show that this method can effectively identify the signals buried in the noise and improve the accuracy and reliability of fault diagnosis.This research provides new insights and methods for fault monitoring of tapered roller bearings,which is of great significance for ensuring the safe and stable operation of mechanical equipment.关键词
轴承/故障诊断/多头注意力/噪声/神经网络Key words
bearings/fault diagnosis/multi-attention/noise/neural network分类
机械工程引用本文复制引用
田召阳,王风涛,熊元昊,王子豪,钱居楠..噪声干扰下基于多头注意力机制的圆锥滚子轴承故障诊断[J].井冈山大学学报（自然科学版）,2025,46(6):95-102,8.基金项目
国家自然科学基金项目(51905001) （51905001）
安徽未来技术研究院企业合作项目(2023qyhz22) （2023qyhz22）
安徽工程大学校级项目(Xjky2022012) （Xjky2022012）
