摘要
国内能源生产装置规模大型化发展趋势明显,与其配套的旋转机械设备发生故障导致的非计划停机将会造成严重的经济损失与重大安全问题。转子不平衡贯穿了旋转机械设备的整个生命周期,服役转子的状态诊断格外重要。针对大型旋转机械振动测点较多,振动信号具有非平稳特征等问题,提出基于多源域数据提取与机器学习算法的转子不平衡故障诊断模型。首先以多源振动监测数据为驱动,根据互相关系数提取故障信息丰富的振动信号,融合时域、频域、时频域等多域特征构建高维混合特征空间;其次利用基于t分布的随机邻域嵌入方法揭示高维空间的特征信息,反映为可视化的三维空间;最终通过最邻近节点算法进行故障分类,判断转子的不平衡质量与相位。本文提出利用互相关系数表征多源数据的故障信息丰富程度,并结合机器学习手段判断转子不平衡类型。通过设计不同附加质量的转子在多转速下不平衡状态实验,验证了所提模型的有效性,解决了转子在线诊断和现场动平衡问题。
The trend toward large-scale development of domestic power generation equipment is evident,and unplanned downtime caused by the failure of its supporting rotating mechanical equipment will cause serious economic losses and major safety issues.Rotor imbalance runs through the entire life cycle of rotating mechanical equipment,and diagnosing the condition of an in-service rotor is particularly important.A rotor imbalance fault diagnosis model based on multi-source domain data extraction and machine learning algorithms is proposed to address the problems of large rotating machinery with multiple vibration measurement points and non-stationary vibration signals.Based on multi-source vibration monitoring data,a vibration signal with rich fault information is first extracted based on cross-correlation coefficients,and a high-dimensional mixed feature space is constructed by fusing multi-domain features such as time domain,frequency domain and time-frequency domain.Secondly,a random neighbourhood embedding method based on t-distribution is used to reveal the feature information of the high-dimensional space,which is reflected as a visualised three-dimensional space.Finally,the nearest node algorithm is used for fault classification to determine the unbalanced mass and phase of the rotor.This proposed model uses the cross-correlation coefficients to characterize the richness of fault information in multi-source data,and the combination of machine learning methods to determine the type of rotor unbalance.The effectiveness of the model was verified by designing unbalance state experiments on rotors with different additional masses at multiple speeds,solving the problems of online diagnosis and on-site dynamic balancing of rotors.
作者
关晓晴
卫炳坤
牛东圣
焦瀚晖
胡东旭
张雪辉
XiaoQing GUAN;BingKun WEI;NIU DongSheng;JIAO HanHui;HU DongXu;ZHANG XueHui(Guohua Energy Investment Co.,Ltd.,Beijing 100007;Institute of Engineering Thermophysics,Chinese Academy of Sciences,Beijing 100190;Northwest Engineering Corporation Limited,PowerChina,Xi'an 710065,China)
出处
《北京化工大学学报（自然科学版）》
CAS
CSCD
北大核心
2024年第2期109-119,共11页
Journal of Beijing University of Chemical Technology(Natural Science Edition)
基金
内蒙古重大科技专项(2020ZD0017)
陕西省创新能力支撑计划(2023KJXX-086)。
关键词
转子不平衡
多源域数据
智能故障诊断
旋转机械
rotor imbalance
multi-source domain data
intelligent fault diagnosis
rotating machinery
