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handle: 2117/11518 , 10261/39348
This paper presents a signal analysis methodology to validate (detect) and reconstruct the missing and false data of a large set of flow meters in the telecontrol system of a water distribution network. The proposed methodology is based on two time-scale forecasting models: a daily model based on a ARIMA time series, while the 10-min model is based on distributing the daily flow using a 10-min demand pattern. The demand patterns have been determined using two methods: correlation analysis and an unsupervised fuzzy logic classification, named LAMDA algorithm. Finally, the proposed methodology has been applied to the Barcelona water distribution network, providing very good results. Peer Reviewed
Flow meter, Telecontrol system, :Informàtica::Automàtica i control [Àrees temàtiques de la UPC], Flow meter industry, Water -- Distribution, Water distribution network, Telecontrol, Aigua -- Distribució, Remote control, Àrees temàtiques de la UPC::Informàtica::Automàtica i control, Fuzzy logic classifier, Fault detection, Sensor failure
Flow meter, Telecontrol system, :Informàtica::Automàtica i control [Àrees temàtiques de la UPC], Flow meter industry, Water -- Distribution, Water distribution network, Telecontrol, Aigua -- Distribució, Remote control, Àrees temàtiques de la UPC::Informàtica::Automàtica i control, Fuzzy logic classifier, Fault detection, Sensor failure
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 114 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 1% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
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| downloads | 230 |

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