iSpin
Real multitechnology fleets must be covered by advanced tools, able to provide independent and direct visibility and transparency to asset owners. In many cases classic data analytic tools do not deliver the understanding of the performance of each site, because normally the data owned by operators are very poor base for work (limited in frequency: 10 min samples and limited in channels and variables: especially in mature turbine models).
Turbines are 95% physics, and data analytics need to be linked with design criteria of the turbines. Thanks to physical models we can predict failure modes patterns and track root causes, but to go beyond the existing limited SCADA needs to be used in the most intelligent way, not only for machine learning.
Available data can be made credible measuring the wind adequately; through strategic placed sensorics wecan create a very good set of inputs for turbine complete monitoring, answering the need of brining an independent, easy to understand, transparent and multitechnology tool for active asset management, which monitors asset performance, structural risks, life consumption and cash flows in detail.
This is key for condition based predictive maintenance strategies on all turbine components, in a flexible approach to be embedded within own clients tools.