66-71

• Wang, Huaqing
• Chen, Peng

This paper presents a condition diagnosis method for a blower system using the rough sets, and afuzzy neural network to detect faults and distinguish fault types. In order to solve the ambiguous problembetween the symptoms and the fault types, the diagnosis knowledge for the training of the neural network isacquired by using the rough sets. The fuzzy neural network realized by partially-linearized neural network(PNN), which can automatically distinguish the faults. The PNN can quickly converge when learning, and canquickly and high-accurately distinguish fault types on the basis of the probability distributions of the machineconditions when diagnosing. The non-dimensional symptom parameters are also defined in frequency domain,and those parameters are processed by rough sets to sensitively diagnose machinery conditions. Practicalexamples of the diagnosis for a blower system are shown in order to verify the efficiency of the methodproposed in this paper.

• Economics and Business
• Computer and information sciences

• Επιστήμη
• Business management

condition diagnosis, fuzzy neural network, rough sets, symptom parameter, blower

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