基于PSO-SEBD的风电机组滚动轴承故障诊断  

Fault diagnosis of rolling bearing of wind turbine generator based on PSO-SEBD

作　　者：王鹏程 邓艾东 凌峰[1,2] 邓敏强 刘洋 WANG Pengcheng;DENG Aidong;LING Feng;DENG Minqiang;UU Yang(National Engineering Research Center Power Generation Control and Safety,Southeast University,Nanjing 210096,China;School of Energy and Environment,Southeast University,Nanjing 210096,China)

机构地区：[1]大型发电装备安全运行与智能测控国家工程研究中心,南京210096 [2]东南大学能源与环境学院,南京210096

出　　处：《振动与冲击》2023年第7期281-288,共8页Journal of Vibration and Shock

基　　金：江苏省碳达峰碳中和科技创新专项资金(BA202214);江苏省重点研发计划(BE2020034);中央高校基本科研业务费专项资金资助(3203002201C3)。

摘　　要：滚动轴承作为风电机组传动系统的关键部件,其健康状态监测对整个机组的安全稳定运行至关重要。针对滚动轴承的故障诊断问题,在基于先验未知盲反卷积技术的包络谱重复瞬态循环平稳性提取方法(extracting cyclo-stationarity of repetitive transients from envelope spectrum based on prior-unknown blind deconvolution technique,SEBD)的基础上,提出了一种基于粒子群算法(particle swarm optimization,PSO)寻优的SEBD滚动轴承故障诊断方法,实现SEBD滤波器长度自适应选择。以最大故障特征频率比(characteristic frequency ratio,CFR)作为适应度函数,利用PSO算法对滤波器长度进行寻优;利用获得的最优滤波器长度进行SEBD处理;根据SEBD处理后信号的包络谱特征实现轴承故障的有效识别。通过对仿真信号和德国帕德博恩大学公开轴承故障数据进行分析,验证了PSO-SEBD的有效性。通过与几种常用的诊断方法对比以及噪声环境下分析,表明该方法具有较好的诊断性能和抗噪声能力。As a key component of wind turbine drive system,health status monitoring of rolling bearing is very important to the safe and stable operation of the whole unit.Here,aiming at the problem of rolling bearing fault diagnosis,on the basis of the method of extracting cyclo-stationarity of repetitive transients from envelope spectrum based on priorunknown blind deconvolution technique(SEBD),a SEBD rolling bearing fault diagnosis method based on particle swarm optimization(PSO)algorithm(PSO-SEBD)was proposed to realize adaptive selection of SEBD filter length・Firstly,taking the maximum characteristic frequency ratio(CFR)as the fitness function,the PSO algorithm was used to optimize the filter length.Then,the optimal filter length was used for SEBD processing.Finally,the effective identification of bearing faults was realized according to envelope spectrum characteristics of signals processed with SEBD.The effectiveness of PSO-SEBD was verified by using it to analyze simulation signals and the published bearing fault data of University of Paderborn in Germany.By comparing PSO-SEBD with several common diagnosis methods and analyses in noise environment,it was shown that PSO-SEBD has better diagnosis performance and anti-noise ability.

关 键 词：滚动轴承 故障诊断 盲反卷积技术 粒子群算法 

分 类 号：TH17[机械工程—机械制造及自动化]