Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/4266
Title: Intelligent Fault Diagnosis in Industrial Actuator based on Neuro-Fuzzy Approach
Authors: Lakhmissi Cherroun
Nadji Hadroug
Mohamed Boumehraz
Keywords: « Neuro-fuzzy system », « Hybrid learning », « Fault diagnosis ».
Issue Date: 25-Nov-2014
Abstract: This paper introduces the application of the hybrid approach Adaptive Neuro-Fuzzy Inference System (ANFIS) for fault classification and diagnosis in industrial actuator. The ANFIS can be viewed either as a fuzzy inference system, a neural network or fuzzy neural network (FNN). This paper integrates the learning capabilities of neural network to the robustness of fuzzy systems in the sense that fuzzy logic concepts are embedded in the network structure. It also provides a natural framework for combining both numerical information in the form of input/output pairs and linguistic information in the form of if-then rules in a uniform fashion. The proposed algorithm is achieved by the intelligent scheme ANFIS. This intelligent system is used to model the valve actuator and classify the fault types. Computer simulation results are shown in this paper to demonstrate the effectiveness of this approach for modeling the actuator and for classification of faults for different fault conditions
URI: http://dspace.univ-biskra.dz:8080/jspui/handle/123456789/4266
Appears in Collections:Communications Internationales

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