用于状态监测信号中冲动性的识别与统计量化的系统框架
A systematic framework for the identification and statistical quantification of impulsivity in condition monitoring signals
AI总结：
本研究提出一种用于状态监测信号中冲动性识别与统计量化的系统框架，通过两阶段方法及蒙特卡洛模拟、工业压缩机振动数据验证，为区分有效信号与异常干扰信号提供可扩展工具。
AI中文摘要：
本文提出了一种用于信号中冲动性行为识别与统计量化的综合框架，主要聚焦于状态监测领域。我们专注于评估冲动性，该行为源于正常运行或额外干扰。这种评估在局部损伤检测中至关重要，因为冲动性干扰的存在会显著复杂化机器状态监测过程。为解决冲动性评估问题，我们引入了两阶段方法以提升处理流程的有效性。首先，基于Mann-Whitney统计量，我们提出了一种用于选择“性能最佳”冲动性度量的客观标准，允许在不同信号场景下对各类经典及高级指标进行系统比较。其次，我们建立了一种基于自助法重采样的统计显著性评估正式流程，并定义了一个幅度指数以量化检测到的冲动性强度。该框架通过针对三种参考信号场景的广泛蒙特卡洛模拟进行验证，并应用于工业压缩机的真实振动数据。通过系统化现有度量并提供基于统计的流程，本研究拓展了先前工作，提供了一种可扩展工具，用于区分具有诊断价值的信号与受异常干扰损坏的信号。
英文摘要：
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.
