IoT Enabled Remote Asset Monitoring and Predictive Maintenance

Altizon Systems

IoT Enabled Remote Asset Monitoring and Predictive Maintenance

Altizon Systems Altizon Systems
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A stripper well or marginal well is an oil or gas well that is nearing the end of its economically useful life. In the U.S., oil wells are generally classified as stripper wells when they produce 10 to 15 barrels per day or less for any 12-month period. These wells account for approximately 18% of the U.S. production. The key ask was to design a solution that would connect these wells often located in remote locations and collect information about their performance and operating conditions. 

Spectra Symbol is a leading U.S. based sensor manufacturer, specifically focused on linear sensors and potentiometers, which are smaller and lighter than those of other linear potentiometer manufacturers. Their sensors streamline and optimize the daily processes of customers across a wide range of industries including medical, automotive, aerospace and industrial operations.

Altizon’s Datonis IoT suite was deployed alongside Spectra Symbol’s proprietary sensors.  Datonis Edge was embedded in  Spectra Symbol’s hardware communication gateway. This hardware was deployed at each site. Datonis IoT platform and a custom business application were deployed on the cloud. The Datonis IoT API was leveraged to integrate with all dependent systems.

Datonis Edge: Embedded in the hardware gateway and responsible for reliably transmitting sensor, monitoring and failure data to the Datonis IoT platform

Datonis IoT: It’s a platform for storing and processing all machine data, which acts as the data lake for the implementation. The platform also acts as the data source for the custom application.

Custom App: It’s a business-focused application used for assessing stripper well performance, condition monitoring and analytics, and for delivering specific reports to the various stakeholders.

Mature (technology has been on the market for > 5 years)

Get an insight into parameters that impact line productivity, such as line rates, loss and quality analysis at multiple levels.

Monitor and analyze parameters that are critical to machine health. Optimize machine downtime by predicting failure before it occurs.

It is an open data platform that acts as a repository for all critical processes, to easily integrate IT systems with process data.

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