Abstract

Motivated by the wind turbine anomaly detection, we propose a Bayesian hierarchical model (BHM) for the mean-change detection in multivariate sequences. By combining the exchange random order distribution induced from the Poisson–Dirichlet process and nonlocal priors, BHM exhibits satisfactory performance for mean-shift detection with multivariate sequences under different error distributions. In particular, BHM yields the smallest detection error compared with other competitive methods considered in the article. We use a local scan procedure to accelerate the computation, while the anomaly locations are determined by maximizing the posterior probability through dynamic programming. We establish consistency of the estimated number and locations of the change points and conduct extensive simulations to evaluate the BHM approach. Among the popular change point detection algorithms, BHM yields the best performance for most of the datasets in terms of the F1 score for the wind turbine anomaly detection.

摘要

受风力涡轮机异常检测的启发，我们提出了一种贝叶斯层次模型（BHM），用于多变量序列中的均值变化检测。通过将泊松-狄利克雷过程引入的交换随机顺序分布和非局部先验相结合，BHM 在不同误差分布下的多元序列均值偏移检测方面表现出令人满意的性能。特别是，与本文中考虑的其他竞争方法相比，BHM 产生的检测误差最小。我们使用局部扫描程序来加速计算，而异常位置是通过动态规划最大化后验概率来确定的。我们建立了估计的变化点数量和位置的一致性，并进行了广泛的模拟以评估 BHM 方法。