CAJ | 학술논문

提出了一种基于多重分形与近似熵相结合的信号特征量提取方法，应用于齿轮箱的故障信号诊断中。针对齿轮箱的故障信号的复杂性，先用减法聚类对提取到的信号特征量进行处理，得到初始聚类中心，然后再用模糊 C 均值聚类（FCM）作进一步处理，实现齿轮箱故障的自动诊断和识别。多重分形谱提取的特征量如谱宽，可以表示信号的波动程度，而近似熵可以表示信号的复杂程度。两者结合可以得到更加准确的齿轮箱故障信号模式。减法聚类可以有效解决 FCM容易陷入局部最优的问题，还可以提高收敛速度。提取的特征参数作为聚类分析的数据，通过计算数据点与聚类中心的隶属度判定所属类型，实现齿轮箱故障类型聚类以及模式识别。通过风力发电机齿轮箱故障诊断实验，证明该方法的可行性和有效性。为齿轮箱故障诊断提供了一种新的有效途径。
제출료일충기우다중분형여근사적상결합적신호특정량제취방법，응용우치륜상적고장신호진단중。침대치륜상적고장신호적복잡성，선용감법취류대제취도적신호특정량진행처리，득도초시취류중심，연후재용모호 C 균치취류（FCM）작진일보처리，실현치륜상고장적자동진단화식별。다중분형보제취적특정량여보관，가이표시신호적파동정도，이근사적가이표시신호적복잡정도。량자결합가이득도경가준학적치륜상고장신호모식。감법취류가이유효해결 FCM용역함입국부최우적문제，환가이제고수렴속도。제취적특정삼수작위취류분석적수거，통과계산수거점여취류중심적대속도판정소속류형，실현치륜상고장류형취류이급모식식별。통과풍력발전궤치륜상고장진단실험，증명해방법적가행성화유효성。위치륜상고장진단제공료일충신적유효도경。
A feature extraction method based on multifractal approximate entropy was presented and used in gearbox fault diagnosis.Considering the complexity of gearbox fault data,the subtractive clustering was used to obtain an initial cluster center of characteristics.Then the fuzzy C-means clustering(FCM)was used for further processing to achieve automatic gearbox fault diagnosis and identification.The volatility of a signal was expressed by feature values extracted by multifractal spectrum,such as spectral width,and the complexity of the signal was represented by approximate entropy (ApEn).The combination of the two representations can make the patterns of gearbox faults more accurate.The problem of easily falling into local optimum during the FCMclustering process was effectively solved by applying subtractive fuzzy clustering,which also improves the convergence rate.The characteristic parameters extracted were taken as the data used in clustering analysis.In order to achieve gearbox fault clustering and recognition,the membership grades of data points and cluster center were calculated.To prove the feasibility and effectiveness of the method proposed,fault diagnosis experiments on a wind turbine gearbox were implemented.The study provides a new effective way for gearbox fault diagnosis.