Maximise Energy Production
Understand at any given time how your assets should be performing
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Prevent Unplanned Downtime
Identify and understand developing issues in real-time
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We build the worlds most powerful digital twins so you can
Regain control of your assets

We enable industry and energy leaders to regain control of their increasingly complex asset base. We do this by applying the worlds most powerful digital twin technology. This unlocks insights into performance, developing failures and unprecedented level of control.

UNLOCK SUBSTANTIAL VALUE

Reduce downtime
Optimise production
Optimise output
Extend life
Trusted by customers across the globe
“Jungle has been an open and collaborative partner for EDP. What really impressed us in working with them was how well they understood the practical aspects - the electrical engineering domain - of our assets. They were able to pair that understanding with their strong machine learning expertise, which really enabled us to make great progress in a very short amount of time.”

LUÍS CORDEIRO SENIOR PROJECT MANAGER AND DATA SCIENTIST, EDP – DIGITAL GLOBAL UNIT

WIND

Wind Intelligence

We provide a clean standardised cloud based data layer for all data in your wind portfolio that you own. It serves as a single source of truth that drives a powerful digital twin engine. This engine provides insight into what performance is and what it should be. Allowing you to focus your efforts to quickly increase output and availability.

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PROCESS INDUSTRY

Process Intelligence

We digitally create a mirror of critical process telemetry in a cloud platform that you own. We provide an application which continuously evolves to understand normality in the monitored process. This allows operations & maintenance crews to identity developing abnormalities and understand their complex origins. This is the key missing ingredient allowing you to get to optimal performance.

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Our approach to predictive intelligence.

We focus on mission-critical processes. To monitor operations that run 24/7 like continuous production lines and onshore/offshore wind farms, you need an approach that scales. Traditional threshold or condition monitoring approaches as well as individual component modelling are labour intensive and might miss emerging problems. Our approach focuses on understanding the normal behaviour of the entire system.

  • INGRESS

    We aggregate, automatically clean and enrich all raw telemetry data from the monitored assets and consolidate into a central dataset, hosted in a cloud-based infrastructure. Existing customer data infrastructure can be used if desired, in combination with Jungle technology to ensure data quality and readiness for modelling.

  • INTELLIGENCE

    We apply Jungle’s proprietary machine learning applications to develop a normality model of the monitored assets. This enables real-time assessment of their performance and health, by understanding at every moment if and how any component is deviating from normality.

  • EMPOWERMENT

    We deploy our platform to provide actionable insights to enhance the reliability and performance of the monitored assets. Concretely, our platform allows the operations, maintenance and asset management teams of our customers to prioritise, monitor and resolve cases around developing abnormality in a collaborative manner. Multiple people can work simultaneously on high priority cases, retrieve visualised information to provide context, share information and plan for action to resolve emerging issues ahead of time.

THE LATEST

Company news

Automatic detection of turbine power curtailment due to component overheating
October 22, 2020

Component overheating impacts wind portfolio revenues. Discover how to detect and minimise its impact on your fleet.

How to give your operators the right alarms (part II)
October 1, 2020

In this post, we present the enhanced failure detections via our multi-sensor based alarms.

Why operators are ignoring SCADA alarms (part I)
September 28, 2020

In part I, we present why SCADA alarms are abundant but not helpful for operators.

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