臺灣博碩士論文加值系統

風扇運轉都會伴隨著振動，當振動過大時，則表示可能發生故障現象，對於風扇而言，在其發生故障時，若使用單一分析方法，有時未能正確診斷出故障原因，透過多種振動分析方法加以診斷，而每一種分析方式，皆有一定鑑別的特性。為了能提高故障鑑別率，傳統典型的診斷是使用感測器量取對象的時域振動信號
，再透過頻域信號作頻譜分析，然後交由專人或有經驗的工程師以時域或頻域兩組信號判斷發生何種故障及其可能性的大小。近幾年來風扇或機械故障判斷技術逐漸由傳統方式轉換成在不需專人判讀的狀況下完成，讓產業達到自動化並節省成本。
本文旨在利用類神經網路中的「倒傳遞演算法」，來使得辨別風扇故障的方法能有所準則，且達到80%以上的辨識率。並藉由此提出的判斷法則，能夠使得風扇不論在內部零件部分故障，或者是外部外殼損壞或不全，皆能有所依偱規則與依據，並藉由此方法使得風扇的生產過程能夠增加產能，提高生產良率。

Mechanical vibrations occur in the normal DC brushless fan working. The large magnitude vibrations may indicate the defects of the fan rolling parts. In this study, an on-line diagnosis system was developed to monitor the fan vibration and judge the failure conditions. Due to the complex mechanical interferences, spectrum analysis results can’t give enough information for the experimental judge of the fan failures. The artificial neural networks analysis was employed for the on-line diagnosis system to realize the automatic fan defects. The networks weighting was performed by the back propagation algorithm. After well system trainingprocesses, the on-line system can judge the DC brushless fan failures according to the vibrations spectra and the 80% hit rate can be achieved.

摘 要 i
ABSTRACT ii
誌 謝 iii
目 錄 iv
表目錄 vii
圖目錄 vii
第一章 緒論 1
1.1 前言 1
1.2 研究目的 2
1.3 文獻回顧 3
1.4 研究方法 4
第二章 信號處理模式 5
2.1 加速規感測器介紹 5
2.2 信號處理 6
2.2.1 傅立葉轉換公式 7
2.2.2 快速傅立葉轉換 8
2.3 信號取樣模式 10
2.3.1 反假像濾波器 10
2.3.2 取樣頻率方式 11
2.3.3 信號洩漏效應 13
第三章 類神經網路架構 15
3.1 神經網路簡介 15
3.3.1 人工神經元原理 17
3.2 倒傳遞類神經網路的架構 18
3.2.1 倒傳遞類神經網路架構 18
3.3 倒傳遞類神經網路的理論 21
3.3.1 錯誤更正法則 23
3.3.2 倒傳遞網路間的修正量 25
3.3.3 倒傳遞網路訓練與辨別方法 23
第四章 實驗方法與結果 27
4.1 實驗步驟 27
4.2 類神經訓練方法與過程 33
4.3 實驗結果 35
4.3.1 訓練目標及結果 35
第五章 結論與討論 48
5.1 結論 48
5.2 未來研究及建議 49
參考文獻 50
附錄
A MEMS加速度感測器振動頻譜圖 53
B 傳統壓電式加速規感測器振動頻譜圖 72

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