Real-time detection of irregular events in periodic data streams
Real-time and adaptive anomaly detection algorithm for cyclostationary models
Machine Learning
Summary
Some data has patterns that repeat over time, but these patterns can hide problems or errors. The authors created a new method called PeriodicCALM that can spot true unusual events without getting confused by normal repeating patterns. It works quickly as the data comes in and adjusts itself over time to stay accurate. Tests with both simulated and real data show it works better and faster than older methods.
What this means in practice
- •For industrial maintenance teams: Improve real-time monitoring of machinery vibrations to more accurately detect faults without confusing routine cyclic behaviors.
- •For network operations centers: Enhance detection of abnormalities in cyclic network traffic patterns by reducing false alarms triggered by regular periodic signals.
Authors
Justyna Witulska, Tomasz Barszcz, Ireneusz Jabłoński, Agnieszka Wyłomańska
Abstract
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.