An evolving approach for fault diagnosis of dynamic systems

This work proposes a methodology for fault identification of dynamic systems using an online evolving approach. The proposed methodology is divided into three stages: pre-processing, processing, and post-processing. The central part of our approach concerns the processing itself, in which we use an online learning evolving algorithm, named AutoCloud, for clustering the different types of faults. The proposal has been validated using data from a real-level control process on a pilot scale. The obtained results indicate that our proposal is adequate for fault identification of dynamic systems. •An evolving-based method for Fault detection and Diagnosis in dynamic systems.•Use case in a real pilot plant.•Unsupervised learning approach for FDD.