This paper proposes a holistic modeling scheme for fault identification in distributed sensor networks. The proposed scheme is based on modeling the relationship between two datastreams by means of a hidden Markov model (HMM) trained on the parameters of linear time-invariant dynamic systems, which estimate the specific relationship over consecutive time windows. Every system state, including the nominal one, is represented by an HMM and the novel data are categorized according to the model producing the highest likelihood. The system is able to understand whether the novel data belong to the fault dictionary, are fault-free, or represent a new fault type. We extensively evaluated the discrimination capabilities of the proposed approach and contrasted it with a multilayer perceptron using data coming from the Barcelona water distribution network. Nine system states are present in the dataset and the recognition rates are provided in the confusion matrix form.

Fault Identification in Distributed Sensor Networks Based on Universal Probabilistic Modeling

NTALAMPIRAS, STAVROS
2015

Abstract

This paper proposes a holistic modeling scheme for fault identification in distributed sensor networks. The proposed scheme is based on modeling the relationship between two datastreams by means of a hidden Markov model (HMM) trained on the parameters of linear time-invariant dynamic systems, which estimate the specific relationship over consecutive time windows. Every system state, including the nominal one, is represented by an HMM and the novel data are categorized according to the model producing the highest likelihood. The system is able to understand whether the novel data belong to the fault dictionary, are fault-free, or represent a new fault type. We extensively evaluated the discrimination capabilities of the proposed approach and contrasted it with a multilayer perceptron using data coming from the Barcelona water distribution network. Nine system states are present in the dataset and the recognition rates are provided in the confusion matrix form.
Distributed sensor network; fault diagnosis; fault identification; hidden Markov model (HMM); linear time-invariant (LTI) modeling; water distribution network; Artificial Intelligence; Computer Networks and Communications; Computer Science Applications1707 Computer Vision and Pattern Recognition; Software
File in questo prodotto:
File Dimensione Formato  
15 TNNLS2362015.pdf

Accesso riservato

: Publisher’s version
Dimensione 1.43 MB
Formato Adobe PDF
1.43 MB Adobe PDF   Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1004304
Citazioni
  • ???jsp.display-item.citation.pmc??? 0
  • Scopus 22
  • ???jsp.display-item.citation.isi??? 21
social impact