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Double Tensor-Decomposition for SCADA Data Completion in Water Networks
Supervisory Control And Data Acquisition (SCADA) systems currently monitor and collect a huge among of data from all kind of processes. Ideally, they must run without interruption, but in practice, some data may be lost due to a sensor failure or a communication breakdown. When it happens, given the nature of these failures, information is lost in bursts, that is, sets of consecutive samples. When this occurs, it is necessary to fill out the gaps of the historical data with a reliable data completion method. This paper presents an ad hoc method to complete the data lost by a SCADA system in case of long bursts. The data correspond to levels of drinking water tanks of a Water Network company which present fluctuation patterns on a daily and a weekly scale. In this work, a new tensorization process and a novel completion algorithm mainly based on two tensor decompositions are presented. Statistical tests are realised, which consist of applying the data reconstruction algorithms, by deliberately removing bursts of data in verified historical databases, to be able to evaluate the real effectiveness of the tested methods. For this application, the presented approach outperforms the other techniques found in the literature.
Double Tensor-Decomposition for SCADA Data Completion in Water Networks
Supervisory Control And Data Acquisition (SCADA) systems currently monitor and collect a huge among of data from all kind of processes. Ideally, they must run without interruption, but in practice, some data may be lost due to a sensor failure or a communication breakdown. When it happens, given the nature of these failures, information is lost in bursts, that is, sets of consecutive samples. When this occurs, it is necessary to fill out the gaps of the historical data with a reliable data completion method. This paper presents an ad hoc method to complete the data lost by a SCADA system in case of long bursts. The data correspond to levels of drinking water tanks of a Water Network company which present fluctuation patterns on a daily and a weekly scale. In this work, a new tensorization process and a novel completion algorithm mainly based on two tensor decompositions are presented. Statistical tests are realised, which consist of applying the data reconstruction algorithms, by deliberately removing bursts of data in verified historical databases, to be able to evaluate the real effectiveness of the tested methods. For this application, the presented approach outperforms the other techniques found in the literature.
Double Tensor-Decomposition for SCADA Data Completion in Water Networks
Pere Marti-Puig (author) / Arnau Martí-Sarri (author) / Moisès Serra-Serra (author)
2019
Article (Journal)
Electronic Resource
Unknown
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