With 45 years of experience, we principally serve the energy sector while also extending our reach into the manufacturing and defence industries through data visualisation and predictive maintenance.
Data Visualisation, Condition based Monitoring and Predictive Analysis with custom dashboard.
Identify Priority assets for replacement and rate case planning. Inform risk buy down calculations by incorporating SCADA and asset specific data.
Improve readiness Posture, forecast sustainment needs, and improve mission success with advanced asset health, maintenance and supply models.
Complete Visibility into asset performance across the plant. Identify equipment that needs inspection or maintenance via risk modelling.
Data visualisation: Data visualisation is an essential tool for the energy sector since it helps stakeholders better understand and analyse huge, complicated datasets. Data visualisation tools are crucial for tracking, making decisions, and spotting patterns since energy systems like power grids, renewable energy installations, and smart metres create a tremendous amount of data.
Energy firms show real-time energy consumption, generation patterns, demand-response scenarios, and market trends using interactive dashboards, charts, and graphs. This visualisation aids managers and operators in decision-making, energy distribution optimisation, and efficiency enhancement.
For several purposes, including remote sensing and monitoring of energy infrastructure, image processing is being used more and more in the energy sector. Power lines, solar farms, wind turbines, and other energy assets are photographed by drones with cameras and sensors, providing useful information for inspection and maintenance.
These recorded images can be examined using image processing algorithms to find flaws, damage, or potential safety concerns. For instance, image processing may examine power line insulators, find hotspots on solar panels, and keep track of the health of wind turbine blades. This helps with preventative maintenance, cuts downtime, and increases the dependability and safety of energy assets.
A crucial component of maintenance procedures in the energy sector continues to be condition-based monitoring (CBM). CBM systems continually track the health and performance of assets in real-time by integrating data from numerous sensors and instruments mounted on key equipment.
CBM is essential for power generating facilities, substations, and distribution networks in the energy sector. CBM can detect early indicators of equipment deterioration by monitoring variables including temperature, vibration, pressure, and current load, enabling preventive maintenance measures. This lowers operational costs, increases asset longevity, and minimises unexpected breakdowns.
The change in the energy sector towards data-driven decision-making and resource optimisation is largely facilitated by predictive analysis. Predictive analysis utilises historical data, sensor inputs, weather patterns, and market circumstances to forecast future energy demand and supply by utilising modern analytics and machine learning algorithms.
Predictive analysis in energy production enables operators to foresee variations in demand, optimise power generation, and cut excess capacity, improving overall efficiency and cost-effectiveness. Predictive analysis is a tool used in energy trading and pricing to make precise forecasts about market prices, improve energy trading tactics, and control risks related to energy commodities. Energy firms may make proactive decisions, maximise their energy resources, and put sustainable energy practises into place by utilising predictive analysis.