Abstract：Humans cannot process the vast amounts of monitoring data produced every minute, so data faults may not been seen and the data can become unreliable. Systems are needed to recognize abnormalities and invalid data. This paper describes a data self-recognition approach based on independent monitoring sites. 56 months of monitoring data was collected from one water distribution network in Beijing. The data was cut into different time-slice series for an autoregressive moving average model (ARMA). This provided a prediction confidence interval to identify the outliers in the test data series. The results gave good outlier identification with data feedback correction. Thus, the system provides data quality control with self-recognition that easily and efficiently deals with invalid data.
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LIU Shuming, WU Yipeng, CHE Han. Monitoring data quality control for a water distribution system using data self-recognition. Journal of Tsinghua University(Science and Technology), 2017, 57(9): 999-1003.
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2017, Vol. 57 
