Energy Sector

The energy industry is in a complex transition phase as the world braces to address the challenges of Climate change and environment impact of these industries.

Managing this transition will require these industries to keep a close view of the assets their conditions and their impact on the environment, the industry and the net benefits or setback it delivers. Furthermore unexpected failures and faults can result in the underperformance of a particular renewable energy project which may compromise its financial success and endanger future investment.

DeepDIVE is a platform that is designed to address the asset condition monitoring needs of the energy industry. It has a set of tools that enables consolidating all asset condition data in a single context (The asset), enables processing tools ( AI, 3D, photogrammetry and many more) that will enable speedier and more accurate inspections, and powers the continuous AI Journey of the industry through continuous improvement with a human-expert-in-command approach.

Our key solution segments are:

Wind Energy

Solar Energy

Nuclear Energy
(coming soon)

Oil & Gas
(coming soon)

Why does the Wind Energy Industry need this?

Wind is currently the fastest growing renewable energy source for electrical power generation around the world. Because of the broadly variable loads and the aggressive operating conditions, keeping away from costly breakdowns and pursuing an operation with consistent performance requires implementation of advanced condition monitoring (CM) systems.

Wind Turbines components (such as blades, rotor and shaft, gearbox, yaw system, and the electric generator) are typically are monitored through a varied set of CM mechanisms. visual condition (cracks, Corrosion, Deformations, alignment, tilt), vibration, temperature, torque, Oil Debris, Acoustic emission, Optical Fibre, Current Power, are just some of the condition monitoring needs of a WT.

Each of these data sets are acquired differently and separately. The intelligence and the time taken of being able to contextualize, corroborate and analyze all this data and arrive at critical decisions of maintenance of the WT is still very heavily human dependent, and one human but multiple human experts. This is where DeepDIVE platform comes in. its ability to consolidated, corroborate and contextualize all monitoring data and create a intelligence frame work that enables Asset managers to make predictive and preventive maintenance decisions is the key differentiating factor of our Solution. Our tools for different data processing methods, intelligently creating data context and specific tools that can help enhance the reporting mechanisms for wind turbines, DeepDIVE offers plenty of flexibility and context to improve the asset condition monitoring for Wind Farms both for large scale onshore and offshore farms.

Speak to us to know more about our DeepDIVE platform for Wind Turbines.

Extending DeepDIVE to address Wind Farm Asset Quality management.

Why does the Solar Energy Industry need DeepDIVE?

Just like in Wind farms, Solar farms too need robust mechanisms to determine the state of operation of the whole generation system. This is essential In order to avoid financial losses and to guarantee a robust and reliable power supply for final users

The techniques and strategies for fault identification become of great importance to ensure this robustness since they can detect an abnormal condition either for preventing or correcting any malfunctioning.

Some of the key faults that can be found on Solar panels are classified into electrical faults, weather-related faults, and panel faults.

Each classification group has different fault details that required different methods to gather data and identify faults. What is even more important is that each classification is not a stand along but can cumulatively and corroboratively significantly affect the performance, efficiency and safety of the Solar farm.

With DeepDIVE Solar farm data can be consolidated, can be and analysed specifically for Solar farms. For example, visual and thermal Data can be viewed and processed at the same time. Orthomosaic models and Visual 3 Models can be compared, and then specific underlying data can be dived into and compared with past historical data. DeepDIVE supports additional data to be added into the platform and cunified context of Solar farm condition, enabling maintenance teams to make faster and more accurate informed decisions from current and past data and use AI tools for predictive and preventive maintenance.