发表机构
Wrocław University of Science and Technology; AGH University; Brandenburg University of Technology（弗罗茨瓦夫理工大学; AGH大学; 勃兰登堡工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出PeriodicCALM，一种针对周期平稳数据流的实时异常检测框架，通过引入周期依赖性变化忽略规则脉冲，在模拟和真实压缩机振动信号上优于基线CALM，提升检测精度和训练效率。
AI 中文摘要
本文介绍了PeriodicCALM，一种针对周期平稳数据流设计的高效实时异常检测框架。经典周期平稳过程具有周期性时变的统计特性，而现实世界信号往往包含周期性出现的脉冲成分，这些成分掩盖了异常行为。现有的实时方法难以应对这些动态特性，经常将相位依赖性变化误判为非周期性异常，导致过多的误报。为解决这一问题，PeriodicCALM引入了周期依赖性变化，以系统性地忽略规则的周期性脉冲，同时准确隔离真正的异常。该方法实时运行并具备连续重训练能力，能够动态适应不断演变的信号特征。与基线CALM框架在模拟数据上的对比评估表明，该方法在检测精度和训练效率上均有显著提升，同时降低了预测延迟。此外，PeriodicCALM的实际效用已通过从压缩机监测系统采集的真实振动信号得到验证。
英文摘要
This article introduces PeriodicCALM, an effective real-time anomaly detection framework designed for cyclostationary data streams. While classical cyclostationary processes feature periodically time-varying statistical properties, real-world signals often contain recurring impulsive components that conceal abnormal behavior. Existing real-time methods for struggle with these dynamics, frequently misinterpreting phase-dependent variability as non-cyclic anomalies and causing excessive false alarms. To address this, PeriodicCALM incorporates cycle-dependent variability to systematically ignore regular cyclic impulses while accurately isolating genuine anomalies. Operating in real time with continuous retraining capabilities, the method adapts dynamically to evolving signal characteristics. Comparative evaluations against the baseline CALM framework using simulated data demonstrate significant improvements in detection accuracy and training efficiency, alongside a reduction in prediction latency. Furthermore, the practical utility of PeriodicCALM is validated on real-world vibration signals collected from a compressor monitoring system.
Comments16 pages
