Rostaghi Mostafa, Khatibi Mohammad Mahdi, Ashory Mohammad Reza, Azami Hamed
Modal Analysis (MA) Research Laboratory, Faculty of Mechanical Engineering, Semnan University, Semnan 35131-19111, Iran.
Department of Neurology and Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA.
Entropy (Basel). 2021 Nov 14;23(11):1510. doi: 10.3390/e23111510.
Bearing vibration signals typically have nonlinear components due to their interaction and coupling effects, friction, damping, and nonlinear stiffness. Bearing faults affect the signal complexity at various scales. Hence, measuring signal complexity at different scales is helpful to diagnosis of bearing faults. Numerous studies have investigated multiscale algorithms; nevertheless, multiscale algorithms using the first moment lose important complexity data. Accordingly, generalized multiscale algorithms have been recently introduced. The present research examined the use of refined composite generalized multiscale dispersion entropy (RCGMDispEn) based on the second moment (variance) and third moment (skewness) along with refined composite multiscale dispersion entropy (RCMDispEn) in bearing fault diagnosis. Moreover, multiclass FCM-ANFIS, which is a combination of adaptive network-based fuzzy inference systems (ANFIS), was developed to improve the efficiency of rotating machinery fault classification. According to the results, it is recommended that generalized multiscale algorithms based on variance and skewness be examined for diagnosis, along with multiscale algorithms, and be used to achieve an improvement in the results. The simultaneous usage of the multiscale algorithm and generalized multiscale algorithms improved the results in all three real datasets used in this study.
由于轴承振动信号存在相互作用和耦合效应、摩擦、阻尼以及非线性刚度，其通常具有非线性成分。轴承故障会在不同尺度上影响信号的复杂性。因此，在不同尺度上测量信号复杂性有助于轴承故障诊断。众多研究探讨了多尺度算法；然而，使用一阶矩的多尺度算法会丢失重要的复杂性数据。相应地，近来引入了广义多尺度算法。本研究考察了基于二阶矩（方差）和三阶矩（偏度）的改进复合广义多尺度散布熵（RCGMDispEn）以及改进复合多尺度散布熵（RCMDispEn）在轴承故障诊断中的应用。此外，还开发了将自适应网络模糊推理系统（ANFIS）相结合的多类FCM-ANFIS，以提高旋转机械故障分类的效率。根据结果，建议在诊断中除了考察多尺度算法外，还应研究基于方差和偏度的广义多尺度算法，并用于改进结果。多尺度算法和广义多尺度算法的同时使用改善了本研究中使用的所有三个真实数据集的结果。
