The operation of ball bearings under varying faulty conditions comprises complex time-varying modulations in the acquired vibration signals. In such circumstances, the extraction of nonlinear dynamic parameters based on multiscale fuzzy entropy (MFE) and refined composite multiscale fuzzy entropy (RCMFE) have proved to be more efficient in fault recognition than the conventional feature extraction methods. However, the accuracy of the methods in classifying several fault classes should not arrive at the expense of higher computational cost. The two major factors responsible for affecting the computational cost are the sampling length and number of features. This paper investigates the capabilities of MFE and RCMFE methods to estimate several health states of bearing at a different range of sampling lengths and scale factors. The bearing condition comprises normal and defective states, where the defective state considers incipient and severe faulty conditions of bearing. The diagnosis capability of both methods is verified by employing the support vector machine classifier. Although the results demonstrate higher fault classification ability of RCMFE for both incipient and severe bearing faults, the results are more impressive, especially at a higher range of scale factors as well as at lower sampling lengths.

球形轴承在变化的故障条件下的运行包括在获取的振动信号中进行复杂的时变调制。在这种情况下，事实证明，基于多尺度模糊熵（MFE）和改进的复合多尺度模糊熵（RCMFE）的非线性动态参数提取比常规特征提取方法更有效。但是，分类几个故障类别的方法的准确性不应以更高的计算成本为代价。影响计算成本的两个主要因素是采样长度和特征数量。本文研究了MFE和RCMFE方法在不同采样长度和比例因子范围内估计轴承几种健康状态的能力。轴承状态包括正常状态和缺陷状态，其中缺陷状态考虑了轴承的初期和严重故障状态。通过使用支持向量机分类器，验证了这两种方法的诊断能力。尽管结果表明RCMFE对早期和严重的轴承故障均具有较高的故障分类能力，但结果却令人印象深刻，尤其是在比例因子范围较大以及采样长度较短的情况下。