Sequential Detection of Multiple Change Points in Networks: A Graphical Model Approach

Amini, Arash; Nguyen, XuanLong

doi:10.1109/tit.2013.2264716

Cited by 13 publications

(11 citation statements)

References 23 publications

(68 reference statements)

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“…When there are multiple damage variables in the system, we can still apply this rule to identify if any of these faults has occurred. Besides detecting whether there is a damaged component in the structure, we are also interested in finding which component is damaged and the number of damaged components [47]. To satisfy these two goals, we extend our proposed damage detection rule in (9):…”

Section: Sequential Damage Detection At Multiple Locationsmentioning

confidence: 99%

“…However, as we discussed above, this process is energy inefficient. If the graphical model is a tree, we can use the generalized distributive law (GDL) or the belief propagation algorithm to produce exact values of posterior probabilities [47,50,53]. In the GDL algorithm, every sensor only needs to send a "message" to its neighbors.…”

Section: Distributed Damage Detection: Message-passing Algorithmmentioning

confidence: 99%

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Sequential Multiple Structural Damage Detection and Localization: A Distributed Approach

Liao¹,

Rajagopal²

2018

Preprint

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As essential components of the modern urban system, the health conditions of civil structures are the foundation of urban system sustainability and need to be continuously monitored. In Structural Health Monitoring (SHM), many existing works will have limited performance in the sequential damage diagnosis process because 1) the damage events needs to be reported with short delay, 2) multiple damage locations have to be identified simultaneously, and 3) the computational complexity is intractable in large-scale wireless sensor networks (WSNs). To address these drawbacks, we propose a new damage identification approach that utilizes the time-series of damage sensitive features extracted from multiple sensors' measurements and the optimal change point detection theory to find damage occurrence time and identify the number of damage locations. As the existing change point detection methods require to centralize the sensor data, which is impracticable in many applications, we use the probabilistic graphical model to formulate WSNs and the targeting structure and propose a distributed algorithm for structural damage identification. Validation results show highly accurate damage identification in a shake table experiment and American Society of Civil Engineers benchmark structure. Also, we demonstrate that the detection delay is reduced significantly by utilizing multiple sensors' data.

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Section: Sequential Damage Detection At Multiple Locationsmentioning

confidence: 99%

Section: Distributed Damage Detection: Message-passing Algorithmmentioning

confidence: 99%

Sequential Multiple Structural Damage Detection and Localization: A Distributed Approach

Liao¹,

Rajagopal²

2018

Preprint

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“…Fig. 11 shows the comparative analysis of cells 3,4,5,6,8,9 in the Lushan area from September 28, 2012 to September 27, 2013. As Figs.…”

Section: B Lushan Earthquakementioning

confidence: 99%

“…Amini and Nguyen modeled and detected the multiple change points in networks using a probabilistic formulation. The proposed method includes a class of sequential detection rules for certain functional change points that can detect changes in networks by these rules [3]. Ho and Wechsler proposed a Martingale framework for detecting changes in data streams [4]- [6].…”

Section: Introductionmentioning

confidence: 99%

Detecting Seismic Anomalies in Outgoing Long-Wave Radiation Data

Kong

Glass

2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In this paper, we propose a Geometric Moving Average Martingale (GMAM) method for change detection. There are two components underpinning the method which enable it to reduce false detections. The first is the exponential weighting of observations to obtain the GMAM value and the second is the use of the value for hypothesis testing to determine whether a change has occurred. Extension of the GMAM method to the average GMAM (AG) method has been applied to analyze seismic anomalies within outgoing long-wave radiation (OLR) data observed by satellites from 2006 to 2013 for the two recent Wenchuan and Lushan earthquakes and four comparative study areas: Wenchuan, Puer, Beijing, and Northeastern areas. The Yushu earthquake and Hetian earthquake have also been examined. The experimental results show that the proposed AG method can effectively extract abnormal changes within OLR data and that there are large AG values in the pre and postoccurrence of the earthquakes in these areas, which could be viewed as seismic anomalies, and the AG method has experimentally compared with the deviation method. The experimental results show that the AG method can effectively reflect the change process in OLR data.

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“…We provide a proof sketch for the case d = 2 here (the full proof is quite technical, and can be found in [14]). Fix some φ = τ S and consider the likelihood ratio…”

Section: Appendix a Proof Sketch Of Theoremmentioning

confidence: 99%

Message-passing sequential detection of multiple change points in networks

Nguyen

Amini

Rajagopal

2012

2012 IEEE International Symposium on Information Theory Proceedings

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We propose a probabilistic formulation that enables sequential detection of multiple change points in a network setting. We present a class of sequential detection rules for functionals of change points, and prove their asymptotic optimality properties in terms of expected detection delay time. Drawing from graphical model formalism, the sequential detection rules can be implemented by a computationally efficient messagepassing protocol which may scale up linearly in network size and in waiting time. The effectiveness of our exact and approximate inference algorithms are demonstrated by simulations.

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Sequential Detection of Multiple Change Points in Networks: A Graphical Model Approach

Cited by 13 publications

References 23 publications

Sequential Multiple Structural Damage Detection and Localization: A Distributed Approach

Sequential Multiple Structural Damage Detection and Localization: A Distributed Approach

Detecting Seismic Anomalies in Outgoing Long-Wave Radiation Data

Message-passing sequential detection of multiple change points in networks

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