Temporal/spatial model-based fault diagnosis vs. hidden Markov models change detection method: application to the Barcelona water network
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Document typeConference report
Defense date2013
Rights accessRestricted access - publisher's policy
Abstract
This paper deals with a comparison of two differ-
ent fault diagnosis frameworks. The first method is based on
a temporal/spatial model-based analysis by exploiting
a-priori
information about the system under study, so fault detection is
based on monitoring the residuals of combined spatial and time
series models obtained from the network. The second method
aims at characterizing and detecting changes in the probabilistic
pattern sequence of data coming from the network. Relation-
ships between data streams are modelled through sequences of
linear dynamic time-invariant models whose trained coefficients
are used to feed a Hidden Markov Model (HMM). When the
pattern structure of incoming data cannot be explained by the
trained HMM, a change is detected. Here, the performance
obtained from this two distinct approaches is examined by using
a dataset coming from the Barcelona water transport network.
CitationQuevedo, J. [et al.]. Temporal/spatial model-based fault diagnosis vs. hidden Markov models change detection method: application to the Barcelona water network. A: Mediterranean Conference on Control & Automation. "MED 2013: 21st Mediterranean Conference on Control & Automation (MED): conference digest: June 25-28, 2013: Minoa Palace Resort & Spa, Platanias, Chania, Crete, Greece". Platanias-Chania, Crete: 2013, p. 394-400.
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