Using Fuzzy C-means and Fuzzy Intergals for Machinery Fault Diagnosis
نویسنده
چکیده
This research applied fuzzy c-means and fuzzy integral theories to a proposed novel two-step machinery fault diagnosis model. Distributed multiple fuzzy c-means classifiers were used to produce an initial diagnosis result by considering different features. Fuzzy measure and fuzzy integral data fusion theory was then applied to combine the initial diagnosis results into a consensus final decision. Vibration signals from rolling element bearings were used to validate the method. Results showed that the proposed approach using fuzzy c-means and fuzzy integral techniques improved the diagnosis accuracy and reduced the computation load.
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