馬達故障診斷之模糊類神經網路

Motor Fault Diagnosis by Using Fuzzy Neural Network

趙安民(An-Ming Chao)

指導教授 ： 康淵

中原大學/工學院/機械工程研究所/碩士(2004年)

https://doi.org/10.6840/cycu200400629

摘要

本文使用隸屬度函數與類神經網路建構模糊類神經網路應用於馬達故障診斷，根據東元電機公司提供近五年來的出產馬達故障檢修資料建立馬達故障類型與頻譜特徵關係，作為模糊診斷與類神經網路學習的依據。使用頻譜分析儀量測馬達的振動信號，經快速傅立葉轉換為頻域信號，提取頻譜特徵並經隸屬度函數分級後，再以類神經網路推理完成診斷。本文以四個診斷實例，利用模糊類神經網路進行診斷，並與貼近度診斷、類神經網路診斷相互比較，證明模糊類神經網路對於具有多重的混合性故障有較佳的診斷結果。

關鍵字

模糊類神經網路 ； 貼近度 ； 故障診斷 ； 類神經網路

並列摘要

Using membership function and neural network (NN) develops a motor diagnosis system based on fuzzy neural network (FNN) in this paper. According to literature and experience principle, the relationship between fault and frequency spectrum is built up as the rule of fuzzy diagnosis and the training data of NN. In experiment, to use spectrum analyzer pick up the vibration signal of motor, then transform the signal into frequency domain by fast Fourier transforms and classify it by membership function, and detect this classified data in NN. In this paper, comparing with the detect result of FNN, fuzzy, and NN in two examples prove that FNN is better than other methods for multiple fault diagnosis.

並列關鍵字

fault diagnosis ； fuzzy neural network ； membership function ； neural network ； approximate reasoning

參考文獻

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