On-line adaptive clustering for process monitoring and fault detection

An adaptive clustering procedure specifically designed for process monitoring, fault detection and isolation is presented in this paper. The key feature of the proposed procedure can be identified as its underlying capability to detect novelties in the system’s mode of operation and, thus, to identify previously unseen functioning modes of the process. Once a novelty is detected, relevant informations are used to enrich the knowledge-base of the algorithm and as a result the proposed clustering procedure evolves and learns the new features of the monitored process in accordance with the available process data. The suggested clustering procedure is theoretically illustrated and its effectiveness has been investigated experimentally. Particularly, the on-line implementation of the algorithm and its integration with a fault detection expert system have been considered by making reference to a pneumatic process.

► Proposes an adaptive, recursive clustering procedure capable for novelty detection. ► Presents a real-time implementation of the proposed adaptive classifier. ► Illustrates capability and performance of the classifier in the case of fault detection and process monitoring. ► The proposed expert system is capable of identifying novel, previously unseen, working condition on line.