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.
LEADERS IN PREVENTIVE AND PREDICTIVE MAINTENANCE
SensoLeak has developed an innovative breakthrough solution for the Oil and Gas industry - the Early Leakage Detection System, also known as ELDS™
ABOUT
TECHNOLOGY
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.
INDUSTRIES & APPLICATIONS
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Oil, Gas, and Water Pipelines – leak and spill prevention, theft, predictive maintenance
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Turbines – inefficiency of power generation
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Liquid pumps – cavitations and vibrations
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Motors – shaft misalignment
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Pharmaceutical – chemical reactions
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Petrochemical – physical and chemical influences
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Heavy Machinery – pre-detection of shaft malfunctions
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Any industry with stationary processes (including those with low levels of stationarity)
COMPETITIVE ADVANTAGES
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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.
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The system’s self-learning algorithm adapts to changes in the environment and does not require any human intervention.
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A low rate of false alarms made possible through the use of explanatory variables.
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The ability to acquire and process data from several types of sensors, such as acoustic, seismic, electromagnetic, mechanical, chemical, thermal, etc.
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Scalable software – no need to install additional sensors or equipment.
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24/7 online operations with no need for calibration even after unreported changes in the system’s operating conditions.
PROJECTS
Leakage and damage prevention
Gas Leakage Prevention
Oil Leakage and theft prevention
Wind turbine predective maintenance
Contact
To apply for a job with Sensoleak, please send a cover letter along with your CV/résumé to: joinus@sensoleak.com
Leave A Note
1-714-232-6660
info@sensoleak.com
15990 N Barkers Landing Rd.
Houston, Texas 77079