At i4SEE, we harness augmented intelligence’s power to develop high-performance and low-cost digital applications that you can implement fast. Whether you want to improve monitoring, identify and manage load-based reliability prognostics, or streamline your way of working – you’ll find a product to meet your needs. We design i4SEE applications to help you navigate risk, manage workload, and reduce maintenance costs.
Data scientists can do amazing things with large amounts of data. However, we all remember the old expression “garbage in, garbage out” right? Every data scientist will tell you that data cleaning is an essential first step for any thorough analysis. The problem is that data checking and cleaning are very time-consuming.
On average 70% of the time spent on a typical data science project is devoted to extracting, preparing and cleaning the data, 20% of the time running the calculations, and 10% of the time writing the report. With this in mind, we at i4SEE identified the incredible potential here for automation.
i4SEE DataCheck™ runs data automatically through an automated screening process. Going way beyond simple threshold checks, i4SEE DataCheck™ uses self-learning, type-dependent algorithms.
i4SEE DataCheck™ clearly flags and highlights the following:
This ensures that data scientists or analysts only get high-quality, valid and complete data sets. i4SEE DataCheck™ is fast, automated, effective and accurate. The bigger the data set, the more valuable and powerful it is.
Even a small loss of performance has a big impact on the energy production and financial outcomes for your wind farms. Wind turbine performance is commonly tracked by operators and asset managers using power curves based on 10-minute SCADA logs. It is well understood that the accuracy is limited, but without resorting to costly and complex a full power performance test, the alternatives are limited.
Have you ever asked yourself the following?
At i4SEE we can show you how this is now reality. i4SEE Performance™ represents a step change in the way we approach wind turbine performance analysis. i4SEE Performance™ can be used automatically at fleet level, assess any turbine, any data format, any time. Step into the future of performance analytics with the wind behind your back and i4SEE Performance™ by your side.
A well-designed wind turbine must balance strength, resilience, and reactivity and withstand loads ranging from a light breeze to strong turbulent gusts. It’s a tall order. Numerous mechanical and rotating parts need to be maintained, lubricated and cooled to stay healthy for the entire operational life. Particularly challenging are the main bearings and generator bearings, which transfer high loads and are difficult and expensive to repair.
Operating temperature is a good indicator of component health, as excess heat often indicates friction poor lubrication, poor cooling or increased wear. The challenge is modelling the turbine’s thermal equilibrium to discern what temperatures are normal and what are not.
Using existing sensors and standard 10min SCADA data, i4SEE Heat™ uses physics-based models to predict bearing temperatures, detecting deviation with a high degree of sensitivity. In addition to generating alerts, specific recommendations for follow-up inspection and repair activities are provided.
The upside of using i4SEE Heat™ is our experience. Avoid component failures and save days or weeks of downtime, depending on spare part availability. You’ll also be able to plan maintenance for less windy days, leading to reduced lost production. Take your turbine’s temperature with i4SEE Heat™.
The wind turbine gearbox endures a long and hard service life, transferring high static and dynamic loads through a lightweight and flexible structure, under a range of atmospheric conditions. The reality for too many wind farms even today is that while the wind turbine’s service life is 20+ years, it will have consumed two or three different gearboxes during that period. Condition monitoring systems have evolved to provide early warning of failure and allow preventative maintenance, but such systems are often expensive and require an expert to analyze data and interpret results.
i4SEE GearDrive™ is the first and only application on the market that provides a holistic view of the condition of the gearbox. i4SEE GearDrive™ combines all relevant and available data sources to provide precise results and actionable outputs. Any or all of the following inputs are considered:
All analysis is performed automatically and produces automatically generated diagnostic information and actionable recommendations. Our goal is one turbine, one gearbox, one application: i4SEE GearDrive™.
At i4SEE, we are sometimes asked whether we can provide long-term storage of the results generated by the i4SEE Applications. The answer is a resounding, yes! The i4SEE Archive™ application offers you secure, long-term, and scalable cloud-based storage on the Microsoft Azure platform, physically located in Western Europe. You can request or export some, or all of your data anytime, and with ease, through the i4SEE API. No need to worry about backups or emergency recovery, we’ve got that covered.
If you’re looking to twin convenience and security, then i4SEE Archive™ is your safest bet. Activate it anytime, anywhere and your results are safely stored with us for as long as you need. If you decide later you would prefer to switch to your own on-premise storage, just let us know and we will help you with migration at short notice.
Secure your entire i4SEE data library for as long as you are a customer for a flat monthly fee with i4SEE Archive™.
Companies working in renewables employ talented people and use good tools, but there is a gap between what the data tells us and what actually gets done. Why? Because once predictive analysis has been used to generate insights, the real challenge begins: embedding the results into day-to-day processes and measure outcomes and improvements.
Inspired by agile workflow methodologies, i4SEE Flow™ solves process related issues for our customers. The results of analysis from any of our applications are automatically prioritized and transferred to issues in an online Kanban-style dashboard for the user to view. Each issue can be assigned to a responsible person and tracked through a custom-defined issue management process.
All stakeholders across the organization can immediately visualize and interrogate:
Process bottlenecks become visually apparent, and if upper management asks “what have we accomplished?” the “Completed” section of a kanban board delivers a definitive answer. Today’s challenge is the process itself, so mind the gap between analytics and asset optimization and get into a state of i4SEE Flow™.
Before data analysis can start, data needs to be collected and organized. Data from different sources usually has different naming structures, which can confuse, complicate or completely block the analysis and decision-making process. With large portfolios and frequent transactions, it is not unusual for data to come from one or more second-level SCADA systems.
Data monitoring platforms can have inconsistencies and perhaps a lack of incentive to use standards for consistency with other competing platforms. The time to fix the naming of data signals is before the analysis begins, to avoid errors later.
i4SEE Direct™ solves these common problems in three steps:
Problems with data harmonization are all too common and complexity is rising every year, so let’s make life easier for the data scientists and performance analysts. The timesaving potential is simply massive. With i4SEE Direct™, we do the heavy lifting, so you don’t have to.
Have you ever wondered why some turbines fail earlier or more frequently than others, even though they have the same type and are built using the same components? The answer is often complex, but sometimes surprisingly simple. On the one hand, failure may be caused by quality defects, or mistakes made somewhere along the way during the installation, maintenance or operation of the turbine. On the other hand, it may be simply the case that a frequently failing turbine is subject to higher loads.
Understanding the root cause of failures can be of huge value. Perhaps we should pay more care and attention to specific turbines? Perhaps we should prioritise them in our scheduled service activities? Perhaps certain critical components in highly loaded turbines should be exchanged early and proactively? In order to make informed, data-driven decisions, the operator needs to be able to compare turbine loads at the fleet level.
i4SEE Damage™ uses available SCADA 10-minute signals, carefully selected and aggregated to provide a high-level overview of turbine loading and damage. Long term trends may be assessed for individual turbines, and each can be benchmarked versus others in the whole fleet. The results provide completely new insights into the current and future behaviour of large fleets. At i4SEE we believe the potential of such insights for optimised fleet operations is huge, so why not join us on a journey into the future and add i4SEE Damage™ to your suite of i4SEE applications?
At i4SEE TECH, we believe that an area with huge potential us the analysis of failure logs. Such data, used correctly, can help us to predict repair expenditures, estimate spare parts requirements, make smarter purchasing decisions, and select the right future technologies. The magnitude of saving potential in this area is in the millions to tens of millions of Euros for medium to large fleets.
So why do so many owners essentially work in the dark when answering key questions relating to failure and reliability, when the outcome of an ill-informed decision holds so much risk? The answer is usually simple: failure data collection and classification are complex and time-consuming tasks which were, until now, not considered a priority because the huge potential value of the results was not understood.
It’s time to break this deadlock. The i4SEE Life™ application uses established statistical methods and can be adapted for use with a variety of input data formats and works even with relatively poorly structured input data. Our unique approach allows us to predict the risk of failure for each turbine under various future scenarios. Multiply these insights across fleets, and now performance managers and company management have solid figures upon which to make those multi-million Euro decisions.
i4SEE TECH is supported by the Austrian Wirtschaftsservice
