Data Mining Update
Water Leak

Water Leak Problem

Initial investigations.

  • Initial data mining showed some loggers making multiple recordings resulting in excess battery/ data use. More than 10% of loggers had made at least one recording per month and 5% had made 3 or more in a month.
  • Firmware updated to wait for more than one day of ‘no leak’ before new recording would be made.
Data Mining Update

Initial investigations – continued.

  • Initial data mining showed that of 20000 units deployed, 85% of units have very low level-spread values that indicate not likely to be leaks.
  • In these cases the determination can be stopped after the first minute of the 5 minute determination to make significant power saving of nearly 2Ah (~20%) over 5 years.
  • firmware updated to check result of determination after 1 minute and not continue with the determination as the result will be negative.

This feature can be remotely configured so can be deployed inactive then subsequently activated.

Investigation of audio data using MATLAB.

  • Total of 500 leak, 500 non leak sound files were segregated and analysed using MATLAB.
  • Approaches taken - General audio characteristics, Frequency analysis, Wavelet transformations, Audio fingerprinting. These approaches were unsuccessful in creating a robust classifier.
  • Hence a Machine Learning model becomes necessary to solve the problem of determining leaks from non leaks.
Water Leak


Water No-Leak


Insights and improvements from the model

  • PRV noise is biggest single source of false alarms.
  • PRV noise is highest during the time when we perform measurements – this is because in the night the flow is lowest and PRV valves have to provide highest resistance.
  • Night line improvement – average sensor noise reduces by 4% after system deployed