Fault Detection Based on Mean-Shift Clustering and Immune Danger Theory

Author: David Carson


Modern control systems rely on a complex network of interacting sub-systems. Because a failure in any of these systems could have catastrophic consequences, it is necessary to detect and isolate faults in control system components, i.e. actuators or sensors before the fault is allowed to cause a system failure. Early detection and isolation could enable timely system reconfiguration and increase safety of operations. In the literature, there are numerous fault detection and isolation techniques presented. The central challenge with fault detection is determining the difference between normal and potentially harmful activities in the system. This paper demonstrates the ability of one technique, based on mean-shift clustering and immune danger theory, to detect and isolate faults. The scope of the paper is limited to sensor faults.


mean-shift, danger theory, fault detection, clustering, sensor fault

Volume 19, Issue 1, March 2014, pp. 879-883                    Download PDF


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