Improving clustering based anomaly detection with concave hull: An application in fault diagnosis of wind turbines

Along with the rapid growth of the system complexity, the capability of self-diagnosis is desired by monitoring complex industrial systems to reduce the unplanned system downtimes. By applying data driven analysis methods such as clustering algorithms on the process data of industrial systems, the health status of systems can be deduced and the anomalous statuses can be automatically detected. The accuracy of clustering based anomaly detection using cluster centers is highly dependent on the geometry of the given data set. By a data set with unsymmetrical and concave boundary, using cluster centers as reference to measure the similarity between new observations and clusters normally leads to a high false alarm rate. This paper presented an approach to improve clustering based anomaly detection by building concave hulls for each cluster. For this purpose, a new algorithm for generating n-dimensional concave hulls is developed. The effectiveness of this approach is evaluated with real world data collected from wind turbines.