一种基于关联频繁模式的振动数据流挖掘框架  

Novel Data Mining Framework for Vibration Data Stream Based on Associated Frequency Patterns

作　　者：张艳梅[1] 陆伟[2] 杨余旺[2] Zhang Yanmei;Lu Wei;Yang Yuwang(Department of Electronic Commerce,Guangdong PeiZheng College,Guangzhou,510830,China;School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing,210094,China)

机构地区：[1]广东培正学院电子商务系,广州510830 [2]南京理工大学计算机科学与工程学院,南京210094

出　　处：《数据采集与处理》2019年第5期872-882,共11页Journal of Data Acquisition and Processing

基　　金：国家自然科学基金（61640020）资助项目

摘　　要：针对大型滚转机器轴承故障诊断应用场景中传统故障识别技术通常存在诊断识别精度低的问题,在频域分析基础上提出了一种新的数据挖掘框架——关联频繁模式集挖掘框架(Associated frequency patterns mining framework,AFPMF),由数据预处理、关联频繁模式集挖掘和故障状态监测组成。首先,在数据预处理过程中,AFPMF在时域上使用时间窗分块划分机械振动数据流,再使用傅立叶变换对数据流进行时频变换实现故障频率特征提取。其次,使用基于滑动窗的关联频繁模式树构建压缩树,求解关联频繁模式集,实现数据挖掘过程。最后,根据数据挖掘结果中出现的振动频率判别潜在故障,从而实现监测故障状态。通过对比AFPMF和传统方法在轴承故障诊断应用场景的实验结果可知,相比传统方案,AFPMF具有更优的故障识别性能。In the scenarios of diagnosing bearing faults for large rotary machinery,the traditional fault identification technique usually has the problem of low identification accuracy.Hence,based on the frequency-domain analysis,a novel data mining framework of frequency patterns mining framework(AFPMF)is proposed in this paper,which consists of data pre-processing,associated frequency pattern mining process and fault status monitoring.In the data pre-processing of AFPMF,the time window is adopted to divide the machinery vibration data stream into multiple sub-blocks,and then fast Fourier transform(FFT)is employed to make the data sub-blocks time-frequency transform for frequency feature extraction.The associated frequency pattern tree with sliding window is also used to build a compact tree for data mining.Finally,the potential fault is identified according to the vibration frequency existing in the mining results.Thus the fault status monitoring is realized.The comparison results of AFPMF and the traditional methods in the bearing fault diagnosis show that AFPMF has higher identification accuracy than other traditional ones.

关 键 词：机器状态监测 轴承故障 关联频繁模式树 振动数据流 

分 类 号：TP391.5[自动化与计算机技术—计算机应用技术]