Vibration signals collected from machines can contain rich machine degradation information, such as impulsiveness and cyclo-stationarity. In the field of machine condition monitoring (MCM), quantification of impulsiveness has attracted many researchers’ interests because impulsiveness often indicates an occurrence of incipient faults. Impulsiveness-based health indicators (HIs) (e.g., Gini index, kurtosis, entropy, smoothness index, etc.) are some kinds of statistical parameters that can quantify the impulsiveness of vibration signals. Hence, they have been widely studied during recent years for MCM. However, a thorough comparitive study of those HIs is seldom reported. This paper aims to compare seven impulsiveness-based HIs including kurtosis, skewness, smoothness index, negative entropy, Gini index, Hoyer measure, and the ratio of L2 to L1 norm for MCM according to three properties including the robustness to the length of a signal, the gradient for sparsity or impulsiveness, and quantification of impulsiveness and cyclo-stationarity. Among the seven HIs, it was experimentally found that the Gini index is better than the other indicators to satisfy the three suggested properties for MCM.

从机器收集的振动信号可能包含丰富的机器降级信息，例如脉冲性和循环平稳性。在机器状态监测（MCM）领域中，脉冲性的量化吸引了许多研究人员的兴趣，因为脉冲性通常表示发生了早期故障。基于冲动的健康指标（HIs）（例如，基尼系数，峰度，熵，平滑度指数等）是一些可以量化振动信号冲动的统计参数。因此，近年来，它们已被MCM广泛研究。但是，很少报道对这些HI进行彻底的比较研究。本文旨在比较基于脉冲性的HI，包括峰度，偏度，平滑度指数，负熵，Gini指数，Hoyer度量，以及MCM的L2与L1范数之比，取决于三个特性，包括信号长度的鲁棒性，稀疏性或脉冲性的梯度以及脉冲性和循环平稳性的量化。在七个HI中，通过实验发现，基尼系数优于其他指标，可以满足MCM的三个建议特性。