ABOUT WIND AUDIT
WIND AUDIT specializes in providing independent analysis and technical inspections for wind energy. We have our own early warning system for damage to key components of the wind turbine system based on the diagnosis of vibration and temperature - CMS (Condition Monitoring System). In addition, our offer includes prediction of failures and technical training.
Our team consists of experts in wind power engineering, electrical engineering, electronics, mechanics, IT and machine learning. Thanks to many years of experience and professional skills, we guarantee the highest quality of the projects entrusted to us.
Early detection of wind turbine damage can be planned and acted on, avoiding the major failures and costly downtime. An independent technical audit enables to obtain complete and reliable information on the current technical state of the wind turbine. A transparent inspection report with attached photographic documentation, with a commentary and recommendation on further action, is a valuable piece of information for both the wind turbine owner and the service provider.
Technical inspection of wind turbine
Blades inspection and repair
Thermographic camera inspection
Inspection of service lift and hoist
Power curve analysis
Inspection of MV switchgear
End of warranty inspection (EOW)
Condition Monitoring System (CMS) is a solution for diagnosing critical faults of wind turbine drive train system (main shaft, gearbox, generator) in the early phase. Integrated vibration and temperature sensors located in eight measuring points continuously perform the measurement, and the data provided are indicative of the current level and possible overload. The system operates autonomously, independent from the turbine manufacturer's controller, so it can be successfully installed on any type of wind turbine.
Unscheduled maintenance and breakdowns due to wind turbine component’s failure cause significant production stoppage and thus loss of revenue. In order to minimize costly downtime, it is highly important to manage maintenance and organize work before the failure occurs. By continually wind turbines monitoring, faults can be detected in the initial phase and maintenance actions are planned in a timely manner, avoiding failure and stopping the turbine. Wind turbine failure prediction by machine learning is an innovative way to achieve real benefits in the form of cost reduction (minimizing downtime, maximizing production). We provide a comprehensive analysis of SCADA data using advanced 24/7 machine learning algorithms, so we have the ability to predict an upcoming failure that you will know immediately.
Diagnostics of vibration of industrial drive trains, rotary components, gear box units, shafts, bearings, fans and others. By analyzing the patterns and amplitudes of vibrational noise at certain frequencies, we develop rules and algorithms for diagnosing machine problems. Experienced in wind energy we meet the diagnostic requirements of maintenance services of other industries. With our mobile vibration measurement system we offer a comprehensive vibration analysis service for critical components.
Informational WWW portal with news from the world of wind energy and related fields. New technologies, latest industry information, job offers and more. WIATRAKI.NET will also be released in the paper edition as a quarterly. Also available for mobile devices. Wiatraki = Windmills.
Forecasting wind power energy generation from wind turbines. Meteorological data is not everything. Based on advanced algorithms, we provide a forecast for the wind energy industry. Production forecasting can also be used by Distribution System Operator (DSO) to manage energy distribution, where predictability of production and consumption of energy plays a key role.
A department set up to improve existing products, adding new features and develop new projects which are ultimately responsible for reducing maintenance costs in wind energy and other industries. Development of projects for Industry 4.0. Development of innovative solutions and concepts of future technologies.