CAJ | 학술논문

提出递归定量分析与GG聚类相结合的滚动轴承故障识别方法。利用能够表征信号发散程度的RQA参数———确定率和分层率组成轴承故障识别的特征向量，结合GG模糊聚类实现滚动轴承故障模式识别。对实际故障数据进行分析，结果表明，该方法不仅能够识别滚动轴承的不同程度损伤，而且能够实现不同部位的轴承故障诊断。研究结果为滚动轴承故障识别提供了一种高效、直观的新方法。
제출체귀정량분석여GG취류상결합적곤동축승고장식별방법。이용능구표정신호발산정도적RQA삼수———학정솔화분층솔조성축승고장식별적특정향량，결합GG모호취류실현곤동축승고장모식식별。대실제고장수거진행분석，결과표명，해방법불부능구식별곤동축승적불동정도손상，이차능구실현불동부위적축승고장진단。연구결과위곤동축승고장식별제공료일충고효、직관적신방법。
A fault diagnosis method of rolling bearings based on RQA and GG clustering was put forward .The parameters of the RQA which were able to characterize the degree of divergence of the signal-determinism and laminarity were used to consist the fault feature vector .Combined with the GG fuzzy clustering ,it could achieve the fault pattern recognition of rolling bearings .The analyses of the actual fault data show that the method is able to identify different degrees of damage of rolling bearing faults and to complete different parts of the bearing fault diagnosis .It provides an efficient and intuitionistic new way for the identification of rolling bearing faults .