An Improved Faults Diagnosis Strategy of Analog Circuit Online Using Kernel Fuzzy C-Means for the Unknown Fault

Abstract:

To solve the unknown faults diagnosis online for analog circuit, a novel faults diagnosis strategy based on improved kernel fuzzy c-means (IKFCM) is proposed. simultaneously, the high-performance recognition tree structure of the improved kernel fuzzy c-means itself can decrease the train sample and eliminate wild value, the training speed and precision of classifier can be done well in this way. The realizing of precision fault diagnosis, firstly, via confirming exact class centers from the data of known faults, and then the mean value can be obtained relying on the faults data of each class, meanwhile, setting this mean values as the thresholds for judging faults and each data point is issued with a class label. During the whole faults diagnosis, each detection data will be compared with the thresholds, the high similarity detection data will be fall into the known faults classes while the low similarity detection data will be labeled as unknown faults. Simulation proved the well performance effectively of the proposed IKFCM method .

• Periodical: Advanced Materials Research (Volumes 712-715)
• Pages: 1952-1955
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.712-715.1952
• Online since: June 2013
• Authors: Peng Da Qin*, Yu Ping Qin, Ai Hua Zhang
• Keywords: Analog Circuit, Fault Diagnosis, KFCM, Threshold

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