This paper presents a novel methodology to detect a set of more suitable attributes that may potentially contribute to emerging faults of a wind turbine. The set of attributes were selected from one-year historical data for analysis. The methodology uses the k-means clustering method to process outlier data and verifies the clustering results by comparing quartiles of boxplots, and applies the auto-associative neural networks to implement the residual approach that transforms the data to be approximately normally distributed. Hotelling T2 multivariate quality control charts are constructed for monitoring the turbine’s performance and relative contribution of each attribute is calculated for the data points out of upper limits to determine the set of potential attributes. A case using the historical data and the alarm log is given and illustrates that our methodology has the advantage of detecting a set of susceptible attributes at the same time compared with only one independent attribute is monitored.
