Abstract
In this paper a novel approach to design robust fault diagnosis systems in mechanical systems using historical data and computational intelligence techniques is presented. First, the pre-processing of the data to remove the outliers is performed with the aim of reducing the classification errors. To accomplish this objective, the Density Oriented Fuzzy C-Means (DOFCM) algorithm is used. Later on, the Kernel Fuzzy C-Means (KFCM) algorithm is used to achieve greater separability among the classes, and reducing the classification errors. Finally, an optimization process of the parameters used in the training state by the DOFCM and KFCM for improving the classification results is developed using the bioinspired algorithm Ant Colony Optimization. The proposal was validated using the DAMADICS (Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems) benchmark. The satisfactory results obtained indicate the feasibility of the proposal.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
