This paper proposes a novel fast online methodology for outlier detection called the exception maximization outlier detection method(EMODM), which employs probabilistic models and statistical algorithms to detect abnormal patterns from the outputs of complex systems. The EMODM is based on a two-state Gaussian mixture model and demonstrates strong performance in probability anomaly detection working on real-time raw data rather than using special prior distribution information. We confirm this using the synthetic data from two numerical cases. For the real-world data, we have detected the short circuit pattern of the circuit system using EMODM by the current and voltage output of a three-phase inverter. The EMODM also found an abnormal period due to COVID-19 in the insured unemployment data of 53 regions in the United States from 2000 to 2024. The application of EMODM to these two real-life datasets demonstrated the effectiveness and accuracy of our algorithm.

翻译：本文提出了一种名为异常最大化离群点检测方法的新型快速在线离群点检测方法，该方法采用概率模型和统计算法从复杂系统的输出中检测异常模式。EMODM基于双态高斯混合模型，并在处理实时原始数据而非使用特殊先验分布信息时，在概率异常检测方面表现出强大性能。我们通过两个数值案例的合成数据验证了这一点。对于真实世界数据，我们利用三相逆变器的电流和电压输出，通过EMODM检测了电路系统的短路模式。EMODM还在美国53个地区2000年至2024年的参保失业数据中发现了因COVID-19导致的异常时期。将EMODM应用于这两个现实数据集的结果证明了我们算法的有效性和准确性。