With the development of sensor technology and an avalanche of distributed sensors, the capability to describe patterns and detect change-points is a core skill in system monitoring and prognostics. When data takes the form of frequencies or the number of counts, counting processes such as Poisson processes have been extensively used for modeling. However, most of the existing frequency-based approaches rely on parametric models or deterministic frameworks, thus failing to consider the complex systems’ uncertainties with temporal and environmental contexts. Another challenge is analyzing interrelated multi-sensors simultaneously to detect change-points that cannot be found independently.
This paper presents a multi-output log-Gaussian Cox process (MOLGCP) approach as a frequency-based change-point detection algorithm for real-time monitoring of dynamic systems. MOLGCP models the time-varying intensities of focal events defined over multiple correlated channels in a flexible and interpretable way. Cross-spectral mixture (CSM) kernels are used for model construction to capture both negative and positive correlations as well as the phase difference between channels. Adaptive and scalable decision-making strategies are suggested to identify anomalies in real-time. We show that computational complexities can be reduced and the method can be implemented for online purposes. Finally, extreme value theory (EVT) is used to set up dynamic thresholds considering the correlation between channels. Our method is validated with two different types of datasets: synthetic data and vibration data.
고객의 방문 횟수, 결제 거래 건수 등 데이터가 빈도의 형태를 띄는 경우 포아송 프로세스를 대표로 하는 추계모형을 활용한 분석이 널리 활용되어 왔다. 본 연구에서는 상관관계가 내재된 다중채널에서 관측되는 서로 다른 빈도들을 함께 모델링하는 다중채널 로그가우시안 프로세스를 제시한다. 이를 통해 빈도 기반의 변화탐지 기법을 새롭게 제시하고 시스템 진단 모형으로서의 활용성에 대해 논의한다.