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.
Trying to bring the best machine-learning solution to the energy industry, Our secret recipe combines physics, engineering, computer science, statistics and applied mathematics into one software solution that brings accurate results right to the control centers of O&G operators.
We strive to make it as easy as possible for our users by providing a fully customized product that improves operations, saves on maintenance and provides a holistic overview of all the assets under management.
Over 300 years of industry experience (collectively). Our team possesses all the knowledge and the expertise required to put our technology to work. We welcome diversity and our team reflects that. O&G professionals, pipeline engineers, data scientists, machine-learning experts, algorithm developers, software architects, instruments experts, statisticians, mathematicians, Python developers and more. All come from different backgrounds and together, make our secret recipe work.
Bringing disruptive AI technology and pioneering the IoT connectivity of energy equipment
Drastically reducing false alarms and idle time of pipelines and rotating equipment, hence, prolonging their efficiency and life.
Preserving our planet’s natural beauty and resources.
Environment, health and safety (EHS)
Leak detection and prevention facilitates cleaner land, water and air.
Sensoleak is committed to improving our planet by running a paperless business, using only recyclable items and constantly improving our product to have a truly positive affect on our environment.
Industries and Applications
Oil, Gas, Water Pipelines – leak, spill and theft prevention, real-time predictive analytics
Produced Water - Prevention of LOPC
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.