Meanshift Clustering Based Trend Analysis Distance for Fault Diagnosis

This paper describes a new technique for clustering data based on their trend characteristics. The technique that we propose proceed by incorporating a new distance based on qualitative trend analysis into Mean shift clustering algorithm. Mean shift clustering is a powerful non-parametric technique that does not require prior knowledge of the number of clusters and does not constrain the shape of the clusters. Trend analysis is a data-driven semi-quantitative technique that has been used for process monitoring and fault detection and diagnosis. The performances of our approach are assesed through synthetic banana shaped data. Unsupervised clustering is then applied for intelligent decision-making process specifically for fault diagnosis on Tennessee Easteman Process (TEP) challenge.