Petro Industry News eyes IntelliView as a key solutions provider in an article about the Alberta oil industry, published on the 3rd of January 2019.
The article recognizes IntelliView’s dedication as a “Canadian video analytics specialist” to developing “pioneering solutions” for the industry. Included in the story is a link to an IntelliView pipeline facility monitoring application piece.
The Dual Camera Analytic Module (DCAM™) is IntelliView’s latest solution for early detection and alerting of liquid and wet hydrocarbon leaks, and pipeline integrity remote inspection. The DCAM features an advanced FLIR thermal imaging sensor, a HD visual camera and an on-board processing engine driven by a combination of proprietary and patented analytic software. It is an external leak detection system designed particularly for above ground oil and gas environments.
“Pump stations, pig launchers and pig receivers present a unique challenge because a release of liquid is usually very small and this type of equipment requires a unique coverage of potential leak points,” says Chris Beadle, VP Sales and Marketing at IntelliView. “For a technology to be effective, it needs to provide sufficient coverage of the infrastructure and maintain an acceptable level of detection accuracy, while returning very minimal nuisance alerts.”
The technology platform’s expandable coverage and hardware/software scalability make for a cost-effective investment. A single DCAM has the ability to simultaneously target multiple points of interest, which is typically adequate for standalone configurations. Any number of DCAMs can be added to a system to meet enterprise or multi-site applications, and managed through the centralized IntelliView System Console. The leak solution can also be partnered with other IntelliView software, such as perimeter intrusion detection, to cover complex or multiple needs of a site.
IntelliView systems utilize an industrial IOT architecture. This allows processing power distribution at different points, either at the edge or off-site, based on the network’s capacity and requirements. An example of this is the integration of pre-learned object classification at the control room, to serve as an additional layer of validation and to reduce false alerts.
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