Sensoleak’s real-time monitoring system is based on proprietary machine learning algorithms that analyze streaming data provided by the operators’ systems.
Artificial Intelligence Data Mining
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.
Data Mining Data Analysis
There are several types of data utilized in the identification of pipeline network leakages, including sudden leakages, small and growing leakages, and leakages resulting from maintenance work.
SensoLeak has developed a real-time, sensor agnostic, monitoring system for predicting and identifying evolving failures in various pipelines and mechanical parts. The customized diagnostic system runs parallel to the SCADA and is based on a statistical algorithm that processes data obtained from sensors installed on the diagnosed pipeline/mechanical part. Whenever the machine-learning algorithm receives SCADA data, it calculates the “system health grade” (HG), makes a decision concerning system normality, and outputs alerts as necessary when it identifies deviations from the norm (anomalies). These alerts are very accurate and are delivered to control room operators in any form specified or required by the client. At a Glance.
Industries and Applications
Oil, Gas, and Water Pipelines – leak and spill prevention, theft, predictive maintenance
Turbines – inefficiency of power generation
Liquid pumps – cavitations and vibrations
Motors – shaft misalignment
Pharmaceutical – chemical reactions
Petrochemical – physical and chemical influences
Heavy Machinery – pre-detection of shaft malfunctions
Any industry with stationary processes (including those with low levels of stationarity)
The system reports evolving failures long before the occurrence of an actual casualty event by monitoring both the part or segment in question and the particular variables which may affect the proper operation of such part.
The self-learning algorithm adapts to changes in the environment and does not require any human intervention.
A low rate of false alarms is made possible through the use of explanatory variables.
The system has the ability to acquire and process data from several types of sensors, such as acoustic, seismic, electromagnetic, mechanical, chemical, thermal, etc.
Scalable software eliminates the need to install additional sensors or instruments.
Continuous operations with no need to pause for calibration even after unreported changes occur in the system’s operating conditions
Less than 5% False Alarms
92% Accuracy Rate
The system reports on evolving failures long before the occurrence of a critical events
The system is self-adaptive to changes in the environment and doesn’t require any human intervention.
A low rate of false alarms is achieved by using explanatory variables which describe changes in the explained variable.