@inproceedings{36471f6c4421465192ab383c9c2b4ce7,
title = "A novel pattern-frequency tree approach for transition analysis and anomaly detection in nonlinear and nonstationary systems",
abstract = "The failure to identify anomaly patterns in dynamic systems can cause catastrophic events and incur a high cost. Prior research efforts attempted to use multiple sensors for a closer monitoring of the system dynamics. However, realizing full utilization of multiple sensors without the normality assumptions and dimensionality reduction remains a research challenge to build control schemes. This paper presents a novel methodology of pattern-frequency tree for transition analysis and anomaly detection in nonlinear and nonstationary systems. First, we propose Hyperoctree State space Aggregation Segmentation (HSAS) to delineate the high-dimensional dynamic processes in a continuous state space. Then, we develop a pattern-frequency tree to characterize and model the pattern distribution. Finally, we leverage pattern-frequency distribution information to develop a k-Maximin deviation algorithm for effective and efficient detection of process anomalies. Experimental results demonstrate that the proposed method performs better than the conventional methods in multi-sensor settings and high-dimensional environments.",
author = "Chen, {Cheng Bang} and Hui Yang and Tirupatikumara, {Soundar Rajan}",
year = "2017",
language = "English (US)",
series = "67th Annual Conference and Expo of the Institute of Industrial Engineers 2017",
publisher = "Institute of Industrial Engineers",
pages = "1264--1269",
editor = "Nembhard, {Harriet B.} and Katie Coperich and Elizabeth Cudney",
booktitle = "67th Annual Conference and Expo of the Institute of Industrial Engineers 2017",
address = "United States",
note = "67th Annual Conference and Expo of the Institute of Industrial Engineers 2017 ; Conference date: 20-05-2017 Through 23-05-2017",
}