流体机械2024，Vol.52Issue(9)：98-107,10.DOI:10.3969/j.issn.1005-0329.2024.09.012
基于CNN-VIT模型的非接触式机械密封多源故障状态识别方法
Multi-source fault state recognition of non-contact mechanical seal based on CNN-VIT model
摘要
Abstract
In response to the problems of high-frequency,recognition challenge,and susceptibility to interference of multi-source fault signals of non-contact mechanical seal,a seal test bench was built to simulate four characteristic fault conditions and standard operational conditions.An acoustic emission(AE)system was employed to collect 10 000 feature samples.By combining the Convolutional Neural Network(CNN)architecture with the Vision Transformer model and introducing the Patch concept into the CNN output,a modified model was developed and applied to seal fault state recognition.A novel method for the intelligent recognition of non-contact mechanical seal conditions was proposed,and the influences of the modified model's network layers on the seal AE data.The results show that the AE technique can effectively monitor the multi-source fault state signals of non-contact mechanical seals.This technique can achieve a maximum accuracy rate of 99.42%in recognizing seal conditions.The modified model more notably focuses on the noise frequency band data between 0～50 kHz and 100～150 kHz in the stable operation state signals,as well as the 100～150 kHz noise band data and the 270±40 kHz friction AE band data in the signals indicative of rotating ring surface peeling.The research results can provide a theoretical basis for future development of mechanical seals toward enhanced intelligence,high reliability and long service life.关键词
声发射/非接触式机械密封/故障诊断/深度学习/Vision TransformerKey words
acoustic emission/non-contact mechanical seal/fault diagnosis/deep learning/Vision Transformer分类
机械工程引用本文复制引用
陈金鑫,丁雪兴,陆俊杰,徐洁,张帅..基于CNN-VIT模型的非接触式机械密封多源故障状态识别方法[J].流体机械,2024,52(9):98-107,10.基金项目
国家自然科学基金项目(51905480) （51905480）
宁波市自然科学基金-青年博士创新研究项目(2022J152) （2022J152）
浙江省自然科学基金项目(LY22E050010) （LY22E050010）
"科技创新2025"重大专项(2023Z005,2022Z054,2022Z007) （2023Z005,2022Z054,2022Z007）
宁波市自然科学基金项目(2023J271) （2023J271）
