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Title: An anomaly detection framework for dynamic systems using a Bayesian hierarchical framework

Abstract

Complex systems are susceptible to many types of anomalies, faults, and abnormal behavior caused by a variety of off-nominal conditions that may ultimately result in major failures or catastrophic events. Early and accurate detection of these anomalies using system inputs and outputs collected from sensors and smart devices has become a challenging problem and an active area of research in many application domains. In this article, we present a new Bayesian hierarchical framework that is able to model the relationship between system inputs (sensor measurements) and outputs (response variables) without imposing strong distributional/parametric assumptions while using only a subset of training samples and sensor attributes. Then, an optimal cost-sensitive anomaly detection framework is proposed to determine whether a sample is an anomalous one taking into consideration the trade-off between misclassification errors and detection rates. The model can be used for both supervised and unsupervised settings depending on the availability of data regarding the behavior of the system under anomaly conditions. The unsupervised model is particularly useful when it is prohibitive to identify in advance the anomalies that a system may present and where no data are available regarding the behavior of the system under anomaly conditions. A Bayesian hierarchical settingmore » is used to structure the proposed framework and help with accommodating uncertainty, imposing interpretability, and controlling the sparsity and complexity of the proposed anomaly detection framework. A Markov chain Monte Carlo algorithm is also developed for model training using past data. Furthermore, the numerical experiments conducted using a simulated data set and a wind turbine data set demonstrate the successful application of the proposed work for system response modeling and anomaly detection.« less

Authors:
 [1]; ORCiD logo [2]
  1. Univ. of Miami, Coral Gables, FL (United States)
  2. National Renewable Energy Lab. (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind and Water Technologies Office (EE-4W)
OSTI Identifier:
1513188
Report Number(s):
NREL/JA-5000-72668
Journal ID: ISSN 0306-2619
Grant/Contract Number:  
AC36-08GO28308
Resource Type:
Accepted Manuscript
Journal Name:
Applied Energy
Additional Journal Information:
Journal Volume: 240; Journal Issue: C; Journal ID: ISSN 0306-2619
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
17 WIND ENERGY; 29 ENERGY PLANNING, POLICY, AND ECONOMY; anomaly detection; wind turbine; dynamic systems; sensor-intensive energy; systems; Bayesian modeling

Citation Formats

Moghaddass, Ramin, and Sheng, Shuangwen. An anomaly detection framework for dynamic systems using a Bayesian hierarchical framework. United States: N. p., 2019. Web. doi:10.1016/j.apenergy.2019.02.025.
Moghaddass, Ramin, & Sheng, Shuangwen. An anomaly detection framework for dynamic systems using a Bayesian hierarchical framework. United States. doi:10.1016/j.apenergy.2019.02.025.
Moghaddass, Ramin, and Sheng, Shuangwen. Wed . "An anomaly detection framework for dynamic systems using a Bayesian hierarchical framework". United States. doi:10.1016/j.apenergy.2019.02.025.
@article{osti_1513188,
title = {An anomaly detection framework for dynamic systems using a Bayesian hierarchical framework},
author = {Moghaddass, Ramin and Sheng, Shuangwen},
abstractNote = {Complex systems are susceptible to many types of anomalies, faults, and abnormal behavior caused by a variety of off-nominal conditions that may ultimately result in major failures or catastrophic events. Early and accurate detection of these anomalies using system inputs and outputs collected from sensors and smart devices has become a challenging problem and an active area of research in many application domains. In this article, we present a new Bayesian hierarchical framework that is able to model the relationship between system inputs (sensor measurements) and outputs (response variables) without imposing strong distributional/parametric assumptions while using only a subset of training samples and sensor attributes. Then, an optimal cost-sensitive anomaly detection framework is proposed to determine whether a sample is an anomalous one taking into consideration the trade-off between misclassification errors and detection rates. The model can be used for both supervised and unsupervised settings depending on the availability of data regarding the behavior of the system under anomaly conditions. The unsupervised model is particularly useful when it is prohibitive to identify in advance the anomalies that a system may present and where no data are available regarding the behavior of the system under anomaly conditions. A Bayesian hierarchical setting is used to structure the proposed framework and help with accommodating uncertainty, imposing interpretability, and controlling the sparsity and complexity of the proposed anomaly detection framework. A Markov chain Monte Carlo algorithm is also developed for model training using past data. Furthermore, the numerical experiments conducted using a simulated data set and a wind turbine data set demonstrate the successful application of the proposed work for system response modeling and anomaly detection.},
doi = {10.1016/j.apenergy.2019.02.025},
journal = {Applied Energy},
number = C,
volume = 240,
place = {United States},
year = {2019},
month = {2}
}

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This content will become publicly available on February 20, 2020
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