Sensoleak’s real-time pipeline monitoring system is based on proprietary machine learning algorithms that analyze online data provided by the pipeline’s systems.
Our data mining system analyzes and learns thousands of time- stamped data strings. The system analyzes this large quantity of data by using a proprietary BI engine, which also allows for further dissections and for the generation of reports.
There are several types of data related to pipeline network leakages, and to leakage event identification, such as sudden leakages, small and growing leakages, and leakages resulting from maintenance works.
The system is self-adaptive to changes in the environment and doesn’t require any human intervention.
A low rate of false alarms is attained through the use of explanatory variables that describe changes in the explained variable.
