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Fault Identification in Distributed Sensor Networks Based on Universal Probabilistic Modeling.

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    |October 28, 2014
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    This study introduces a novel fault identification method for distributed sensor networks using hidden Markov models (HMMs). The approach accurately categorizes data as fault-free, known fault, or a new fault type.

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    Area of Science:

    • Computer Science
    • Electrical Engineering
    • Network Engineering

    Background:

    • Distributed sensor networks are crucial for monitoring complex systems.
    • Effective fault identification is essential for maintaining network reliability and performance.
    • Existing methods may struggle with identifying novel or unknown fault types.

    Purpose of the Study:

    • To propose a holistic modeling scheme for fault identification in distributed sensor networks.
    • To develop a system capable of distinguishing between fault-free data, known faults, and novel fault types.
    • To evaluate the proposed method's discrimination capabilities against established techniques.

    Main Methods:

    • Modeling the relationship between data streams using hidden Markov models (HMMs).
    • Training HMMs on parameters of linear time-invariant dynamic systems over consecutive time windows.
    • Representing each system state (including nominal) with a unique HMM.
    • Categorizing novel data based on the HMM yielding the highest likelihood.

    Main Results:

    • The proposed HMM-based scheme demonstrated effective discrimination capabilities.
    • The system successfully categorized novel data into fault-free, known fault, or new fault categories.
    • Performance was evaluated using data from the Barcelona water distribution network, showing promising recognition rates.

    Conclusions:

    • The holistic modeling scheme provides a robust approach for fault identification in distributed sensor networks.
    • The HMM-based method offers an advantage in detecting and classifying new fault types.
    • This approach has significant implications for enhancing the reliability and maintenance of sensor networks.