09/January/2018
Our partner IBM Research (Zurich) has been developing predictive maintenance machine learning technologies for projects spanning servers in data centers to bank automated cash machines
ROMEO Project, led by Iberdrola Renovables Energía, is an industry based consortium made up of 12 recognised and experienced key players from 6 different EU member states. This project, backed by the EU through its H2020 programme, aims to develop advanced technological solutions that enable the operation and maintenance costs of offshore wind power facilities to be reduced.
The partners, chosen to cover the whole value chain, will use the latest technologies, including the cloud, Internet of Things and machine learning, to transition wind turbine maintenance from a calendar-based to a conditioning schedule by analysing the real behaviour of the turbines in use.
Our partner IBM Research Zurich has been developing during last months predictive maintenance machine learning technologies for projects spanning servers in data centers to bank automated cash machines.
During the Technical Committee Meeting gathered in Zurich on December 14, Dr. Dorothea Wiesmann, member of IBM’s Zurich lab, answered few questions about ROMEO Project.
Dorothea Wiesmann
Why it is so important to reduce the maintenance costs of wind turbines?
While wind energy has grown in the EU there is still a way to go to meet the 2030 target of having at least 27% of the EU’s energy consumption coming from renewables. To achieve this we need to reduce the Levelized Cost of Energy (LCoE), which is the net present value of the unit-cost of electricity over the lifetime of an asset, if this case the turbines.
Can you go into the specifics on what you and the team will be contributing from IBM Research?
EU H2020 projects are broken up into various work packages and we are focused on two of them for ROMEO. First, we will be developing advanced machine learning models for predictive maintenance of various wind turbine components for three different wind turbine models operated across three different wind farms. To overcome the limited amount of failure data, we will develop new methods to bootstrap the machine learning models with the engineering models.
In the second work package we will collaborate with project partner INDRA to develop the data acquisition and analysis system that connects the sensors and analytics at the edge with the analytics and cognitive capabilities within IBM’s Cloud with the O&M information management systems to leverage the modeling insights in business decisions.
Why is it so important to analyze the data at the edge, the so called Edge of the IoT?
Most of the sensor data collected today, such as weather, traffic monitoring or even healthcare, is outdated seconds after its collected. In addition, shuttling data from the sensor at the offshore turbine to the cloud not only takes time, but also is limited given the bandwidth to the remote locations. Therefore, if we can analyze some of the data in realtime, where its collected, we can make decisions faster and in some cases automate them, such as shutting down a turbine to avoid cascading damage.
Your previous predictive maintenance research was with computer servers, how much of this can be used for something as different as a wind turbine?
While not quite 1 to 1, it’s all based on a machine learning algorithm. To predict failure in components in cloud data centers we are looking, in essence, at usage data and degradation indicators, e.g. correctable errors, and for the turbines we’d similarly look at system sensor data (vibration, temperature), usage and maintenance history, as well as environmental data.
We then leverage machine learning to understand the drivers and indicators of imminent failures. In addition, working with the subject matter experts in the ROMEO project we can ground these machine learning models in the expertise to combine the best of both worlds.
I realize the project just kicked-off, but what is the ultimate goal?
DW: It is estimated that the O&M of offshore wind contribute between a quarter and a third of the total lifetime cost of energy. Therefore, in the end we want to make sure they as reliable as possible.
Source: IBM Research Zurich