臺灣博碩士論文加值系統

本論文主要是研製一個智慧型馬達旋轉故障診斷系統。此系統的設計是基於常見的馬達旋轉故障，且應用動態結構類神經網路進行鑑別。
系統主體架構可分為：振動訊號量測、濾波處理、故障鑑別。訊號量測部份是將感測設備裝置於待測馬達端，再經由接收器讀取其振動訊號以便後續處理。由於量測的訊號易受外在機械結構的干擾，使得實際量測訊號常附帶有雜訊成份，因此本論文採用小波濾波器建立雜訊消除機制，以降低馬達故障診斷的誤判率。
故障鑑別機制則是採用改良式動態結構類神經網路。以往類神經網路中隱藏層神經元數的選取並沒有標準的理論根據，所以容易造成神經元學習時無法達到最佳網路架構與適時的收斂。因此可調神經元數的網路架構，將可動態調整出最佳鑑別效果的類神經網路。由於馬達故障時，其故障特徵會在特定倍頻中呈現，而且不同故障所擁有的故障特徵也不盡相同。因此本論文利用此種故障特性，擷取特定倍頻做為類神經網路的輸入訊號，而輸出訊號則是馬達故障種類的鑑別結果。
最後，本論文使用MATLAB軟體撰寫整體故障診斷系統，整合訊號量測、濾波、故障特徵擷取以及診斷系統於人機介面中。由實驗結果可知所研製的改良式動態結構類神經網路架構具有良好的鑑別精確度。

This thesis is mainly devoted to developing an intelligent diagnosis system for motor rotary faults. The design of this system is for the common motor rotary faults, and adopts the dynamic structure neural network to establish the diagnosis functionality.
The main structure of system can be divided into three modules: measurement of vibration signal, filtering process and fault classification. The vibration signal measurement is done by mounting the sensing module on the motor cast, and utilizing the receiver to read vibration signal for follow-up processing. Because the measured signal is apt to be influenced by the mechanical structure or other environmental factors, the signal often contains noises. To solve the problem, this thesis adopts the wavelet mechanism to filter the noises out, which may reduce the error rate of fault diagnosis.
The fault classification method uses the improved dynamic structure neural network. For the conventional neural networks, there is lack of methods to determine the number of hidden neurons. It leads that the network structure is not optimal and the convergence problem arises in the learning process. Using the dynamic structure neural network, the optimal neural network could be obtained by adjusting the number of neurons.
For the motor rotary faults, it is known that the characteristics of motor faults appear in specific harmonic frequencies and different faults cause different frequency patterns. This thesis utilizes the fault characteristics and extracts the special frequency patterns as the input of the neural network. The output of the neural network is the classification result of the corresponding fault.
To implement the intelligent fault diagnose system, this thesis uses MATLAB software. The system includes the signal measurement, filtering, fault characteristics extraction, and diagnosis function. All the functions can be executed by using the friendly graphical user interface. From the experimental results, it is found that the classification results outperform than the previous results.

中文摘要 i
英文摘要 ii
誌謝 iv
目錄 v
表目錄 viii
圖目錄 ix
第一章 緒論 1
1.1 研究背景與動機 1
1.2 文獻探討 2
1.3 研究目的與方法 5
1.4 論文架構 5
第二章 馬達旋轉故障與特徵擷取 7
2.1 前言 7
2.2 振動訊號分析 7
2.3 旋轉機械故障分析 10
2.3.1 軸彎曲故障特性 10
2.3.2 不對心故障特性 11
2.3.3 機械鬆動故障特性 12
2.3.4 不平衡故障特性 13
2.3.5 軸承故障特性 13
2.3.6 其他故障特性 16
2.4 故障特徵擷取 18
2.5 本章結論 19
第三章 濾波器簡介 21
3.1 前言 21
3.2 主動式雜訊控制 22
3.3 小波轉換 23
3.4 小波濾波 27
3.4.1 閥值函數 28
3.5 濾波範例 29
3.6 本章結論 31
第四章 改良式動態結構類神經網路 32
4.1 前言 32
4.2 類神經網路簡介 32
4.2.1 神經元的模型 32
4.2.2 類神經網路架構 34
4.3 倒傳遞類神經網路 37
4.4 改良式動態結構類神經網路 41
4.4.1 改良式動態結構類神經網路的結構 41
4.4.2 改良式動態結構類神經網路的演算法 42
4.5 基於不同應用的效能比較 48
4.6 本章結論 60
第五章 使用DSNN於智慧型馬達旋轉故障診斷系統設計與驗證 61
5.1 前言 61
5.2 整體系統架構設計 61
5.2.1 整體系統流程 62
5.2.2 濾波流程介紹 63
5.2.3 故障鑑別機制 64
5.3 模擬結果 66
5.3.1 濾波器模擬比較 66
5.3.2 IDSNN與BPNN的鑑別系統模擬與比較 71
5.4 使用者圖形介面簡介 76
5.5 本章結論 83
第六章 結論與未來展望 84
6.1 結論 84
6.2 未來展望 85
參考文獻 86

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