Ensemble multi-directional anomaly and scenario shift detection for fluid-networks

A data-driven method for continuously monitoring fluid in a fluid network and triggering an alert in case of unexpected characteristics of the fluid, comprising:

• measuring one or more physical quantities at each of three or more measurement units, separately, said physical quantities are relevant to fluid transport, said measurement units are located at three or more different locations in said fluid network, respectively, each of the measurement units comprising one or more sensors for determining one or more measured values of the physical quantities, wherein the physical quantities are time dependent and each of said measured values is determined continuously in a predefined measurement time interval,
• transmitting said measured values to a data processing unit,
• using said data processing unit to determine, for each measurement unit, an observed time series of measured values of each physical quantity, and
• using the data processing unit to process:
• one or more description steps, comprising:
• decomposing each observed time series,
• fitting each observed time series with a time series model, performing one or more statistical hypothesis tests for said time series model, and
• measuring one or more statistical distances between every two different observed time series,
• one or more estimation steps, comprising:
• determining a time-sensitive neural network that is trained based on historical observed time series of the one or more physical quantities from all measurement units, and
• using said time-sensitive neural network to process an estimation:
• estimating, for each measurement unit, an estimated time series of each physical quantity, wherein for each estimation, all measurement units are divided into the measurement unit being estimated and the remaining measurement units, wherein the observed time series of each physical quantity from the remaining measurement units are used to estimate the estimated time series of each corresponding physical quantity from the measurement unit being estimated, the estimated time series comprising estimates for all measured values of each corresponding physical quantity,
• and one or more evaluation steps, comprising:
• determining, for each measurement unit, a deviation between each measured value of each physical quantity and each of said estimates,
• gathering said statistical distances determined from said one or more description steps, if one or more predetermined limit values of said deviation are exceeded,
• quantifying effect of disturbance causing deviation in affected measurement units and rating proximity of measurements units to disturbance source,
• retraining the time-sensitive neural network, if more than one predetermined limit values of said statistical distances are exceeded,
• and outputting an alert, if at least one but not all predetermined limit values of the statistical distances are exceeded.

A system for continuously monitoring fluid in a fluid network and triggering an alert in case of unexpected characteristics of the fluid, including:

• a fluid network,
• three or more measurement units located at three or more different locations in said fluid network, respectively, each of the measurement units comprising one or more sensors for determining one or more measured values of one or more physical quantities, said physical quantities are relevant to fluid transport, and the physical quantities are time dependent and each of said measured values is determined continuously in a predefined measurement time interval, and
• a data processing unit used for receiving said measured values, wherein said data processing unit is configured to determine, for each measurement unit, an observed time series of measured values of each physical quantity, and the data processing unit is configured to process:
• one or more description steps, comprising:
• decomposing each observed time series,
• fitting each observed time series with a time series model,
• performing one or more statistical hypothesis tests for said time series model, and
• measuring one or more statistical distances between every two different observed time series,
• one or more estimation steps, comprising:
• determining a time-sensitive neural network that is trained based on historical observed time series of the one or more physical quantities from all measurement units, and
• using said time-sensitive neural network to process an estimation:
• estimating, for each measurement unit, an estimated time series of each physical quantity, and using said time-sensitive neural network to process an estimation:
• estimating, for each measurement unit, an estimated time series of each physical quantity, wherein for each estimation, all measurement units are divided into the measurement unit being estimated and the remaining measurement units, wherein the observed time series of each physical quantity from the remaining measurement units are used to estimate the estimated time series of each corresponding physical quantity from the measurement unit being estimated, the estimated time series comprising estimates for all measured values of each corresponding physical quantity,
• and one or more evaluation steps, comprising:
• determining, for each measurement unit, a deviation between each measured value of each physical quantity and each of said estimates,
• gathering said statistical distances determined from said one or more description steps, if one or more predetermined limit values of said deviation are exceeded,
• quantifying effect of disturbance causing deviation in affected measurement units and rating proximity of measurements units to disturbance source,
• retraining the time-sensitive neural network, if more than one predetermined limit values of said statistical distances are exceeded, and
• outputting an alert, if at least one but not all predetermined limit values of the statistical distances are exceeded.