Deviation Detection by Self-Organized On-Line Models Simulated on a Feed-Back Controlled DC-Motor

M. Svensson, M. Forsberg, S. Byttner, and T. Rögnvaldsson (Sweden)


Machine learning, fault diagnosis, data mining, mechatronics, deviation detection, state variables


A new approach to improve fault detection is proposed. The method takes benefit of using a population of systems to dynamically define a norm of how the system works. The norm is derived from self-organizing algorithms which generate a low dimensional representation of how selected feature data are correlated. The feature data is selected from the state variables and from the control signals. The self-organizing method and limited number of feature signals enable fast deviation detection and low computational footprint on each system to be analyzed. The comparison analysis between the systems is performed at a service centre, to where the low-dimensional representations of the systems are transmitted. The method is demonstrated on a simulated DC-motor and the results are promising for deviation detection.

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