Self-organizing self-clustering network: A strategy for unsupervised pattern classification with its application to fault diagnosis

In this work, we propose a method for unsupervised pattern classification called self-organizing self-clustering network. This method incorporates the concept of fuzzy clustering into the learning strategy of the self-organizing map. The number of nodes in the network is determined incrementally during the training. The advantage of the proposed strategy over other existing clustering techniques is its ability to determine network size and the number of clusters in data sets automatically. Since the methodology is based on learning, it is computationally less expensive, and the result is not affected by the initial guess. A data set with Gaussian distribution is used to illustrate this method, and results are compared with fuzzy C-mean clustering. Furthermore, the proposed strategy is applied for the fault detection and diagnosis of a twin-continuous tank reactor virtual plant. The result shows that this strategy can be used as a process-monitoring tool in an industrial environment.