齒輪故障診斷之模糊類神經網路

Gear Fault Diagnosis by Using Fuzzy Neural Network

張智傑(Chih-Chie Chang)

指導教授 ： 康淵

中原大學/工學院/機械工程研究所/碩士(2005年)

https://doi.org/10.6840/cycu200500655

摘要

本文將模糊類神經網路應用於齒輪轉子系統的故障診斷，根據既有的文獻與實驗數據建立齒輪故障類型與頻譜特徵關係，作為貼近度診斷與類神經網路學習的依據。文中將具有四項典型故障的齒輪進行振動量測：(1)齒輪偏心(2)軸不平行(3)斷齒(4)磨損。用細化頻譜分析技術提取特徵信號，經過三級隸屬度函數分級，再以模糊類神經網路進行診斷，診斷後的結果與貼近度診斷、類神經網路診斷進行比較。探討在齒輪囓合的複雜頻譜中，模糊類神經網路能作出合理與準確的診斷。

關鍵字

貼近度 ； 齒輪轉子系統 ； 類神經網路 ； 模糊類神經網路

並列摘要

This paper applies fuzzy neural network (FNN) in the fault diagnosis of the gear-rotor system. According to the document and experiment data, the relationship between fault and frequency spectrum is built up as the rule of approximate reasoning diagnosis and the training data of NN. In this paper, gears which have the four typical faults will be taking into vibrating examining : (1)Gear skew (2)Shaft not parallel (3) Tooth breakage (4)wear. Picking up the characteristic signals by using the technique of analysing spectrums . After classifing by membership function, and detect this classified data in NN. Comparing with the detect result of FNN, NN, and approximate reasoning. Verifying that FNN could make rational and accurate diagnosis from the complicated spectrum.

並列關鍵字

reasoning ； approximate ； neural network ； Gear-rotor system ； fuzzy neural network

參考文獻

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