SensoLeak has developed an innovative breakthrough solution for the Oil and Gas industry - the Early Leakage Detection System, also known as ELDS™


SensoLeak has developed a system for predicting and diagnosing evolving failures in mechanical parts. The diagnostic system is based on a statistical algorithm that processes data obtained from sensors installed on the diagnosed mechanical part. Whenever the algorithm receives sensor data, it calculates the “system health grade” (HG), makes a decision concerning system normality and outputs alerts as necessary. These alerts are delivered to the equipment’s operators in any form specified or required by the client. one-page summary.


Machine Learning

Real-time Pipeline Monitoring System

Sensoleak’s real-time pipeline monitoring system is based on proprietary machine learning algorithms that analyze online data provided by the pipeline’s 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 related to pipeline network leakages, and to leakage event identification, such as sudden leakages, small and growing leakages, and leakages resulting from maintenance works.


  • 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 emergence of real ones. This is done by monitoring both the diagnosed part and the set of variables that affect or are involved in its activity.

  • The system’s self-learning algorithm adapts to changes in the environment and does not require any human intervention.

  • A low rate of false alarms made possible through the use of explanatory variables.

  • The ability to acquire and process data from several types of sensors, such as acoustic, seismic, electromagnetic, mechanical, chemical, thermal, etc.

  • Scalable software – no need to install additional sensors or equipment.

  • 24/7 online operations with no need for calibration even after unreported changes in the system’s operating conditions.

24/7 Online Monitoring

<1% False Alarms

98% Accuracy Rate


Leakage and damage prevention

Gas Leakage Prevention

Oil Leakage and theft prevention

Wind turbine predective maintenance


To apply for a job with Sensoleak, please send a cover letter along with your CV/résumé to: joinus@sensoleak.com

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15990 N Barkers Landing Rd.
Houston, Texas 77079