A systematic framework for the identification and statistical quantification of impulsivity in condition monitoring signals

This article proposes a comprehensive framework for the identification and statistical quantification of impulsive behavior in signals, with a primary focus on condition monitoring. We concentrate on evaluating impulsivity, where such behavior results from normal operation or additional disturbances. Such an evaluation is crucial in the context of local damage detection, as the presence of impulsive disturbances significantly complicates the machine condition monitoring process. To address problem of impulsivity assessment we introduce a two-stage methodology to make processing workflow effective. First, we propose an objective selection criterion for "best-performing" impulsivity measures based on the Mann-Whitney statistic, allowing for the systematic comparison of various classical and advanced metrics across diverse signal scenarios. Second, we establish a formal procedure for assessing statistical significance using bootstrap-driven resampling and define a magnitude index to quantify the intensity of detected impulsivity. The framework is validated through extensive Monte Carlo simulations for three reference signal scenarios and applied to real-world vibration data from an industrial compressor. By systematizing existing measures and providing a statistically grounded pipeline, this research extends prior works, offering a scalable tool for distinguishing between diagnostically useful signals and those corrupted by anomalous interference.