GMM Gaussian Mixture Model

In recent years, rapid growth in wind energy as a substantial source of electricity generation has created greater demands on wind turbine system reliability and availability. To reduce service costs and maximize return on investment, wind farm operators have begun to take a more proactive approach to turbine problems by relying on intelligent condition monitoring and automated failure detection systems. The challenge is how to effectively convert large amounts of data into actionable decisions to detect and isolate failures at an early stage. This paper describes a unique data analysis and modeling technique for online turbine health monitoring and automatic root cause assessment. It provides a means to capture failure signatures for specific root causes based on historical events as well as engineering knowledge. Both continuous and discrete turbine condition monitoring data are processed to provide a failure probability assessment. First, statistical trend analysis, feature extraction and classification methods are developed to analyze a continuous sensor data set. Secondly, a pattern recognition method is applied to calculate failure indicators from various discrete control system events, or fault messages. Then failure likelihoods derived from both the continuous and the discrete models are combined in a fusion model to increase predictive accuracy. A demonstration of the method on bearing failure modeling using SCADA data will be provided with promising results