本文主旨在於應用類小腦神經網路(Cerebellar Model Articulation Controller Neural Network)作為診斷技術的理論基礎，並以水循環系統及大型汽輪發電機故障為範例，驗證本論文所提出方法的優越性。以水循環系統為例，依據所收集之故障樣本種類，建構類小腦模式的診斷架構，以梯度衰減法進行記憶權值的訓練。訓練完成的診斷架構即可進行水循環系統的故障診斷。而為了驗證診斷技術的抗雜訊性，文中加入雜訊作測試，測試結果均證明本文所提出方法的可行性及正確性。本文所提出的診斷技術可應用於各型的機電設備系統，對於大型汽輪發電機組的診斷架構，本論文亦作驗證。並提出以微控制器為核心的故障診斷裝置設計，故障資料的收集、記憶權值的訓練及機電設備故障診斷皆可透過此一可攜式診斷器的設計加以實現，不同於習知技術的電腦模擬。最後利用網路傳輸技術開發遠端故障診斷系統，透過遠端資料傳送進行故障診斷，以突破區域性之限制。

The objective of this thesis is to study the applications of cerebellar model articulation controller (CMAC) neural network on the fault diagnosis of mechanical and electrical equipments. To demonstrate the feasibility of the proposed scheme, we take water circulation system and steam turbine generator sets as examples. Depending on the pstterns collection of each possible fault type, we built the diagnosis architecture based on the CAMC neural network firstly. Then a steepest descent learning rule is used to train the diagnosis system until the cost function smaller than a small positive value. Finally, the diagnosis system can be used to diagnose the fault types of water circulation system or turbine generator system. In the case study, the diagnosis results demonstrated the proposed scheme outperforms than traditional schemes on the correctness, noise rejection ability and the learning speed.
In this thesis, the proposed diagnosis system is implemented on a personal computer and microcontroller system simultaneously. On the personal computer, the user can input the diagnosed data by a friendly interface and obtain the diagnosis results. Also, a remote fault diagnosis web site is built in our laboratory to benefit the data collection and diagnosis test for remote users. On the microcontroller system, a portable diagnosis apparatus is implemented to benefit the fault diagnosis on the work field. All the necessary technology described above will be discussed in the thesis.

中文摘要 ---------------------------------------------i
英文摘要 -------------------------------------------iii
誌謝 ------------------------------------------------v
目錄 -----------------------------------------------vi
表目錄 ---------------------------------------------ix
圖目錄 ----------------------------------------------x
符號說明 ------------------------------------------xiv
第一章 緒論---------------------------------------1
第二章 機電設備之故障診斷----------------------------6
2.1 機電設備故障診斷發展-----------------------------6
2.2 故障診斷方法之探討------------------------------7
2.2.1 傳統機電設備故障診斷法-----------------------9
2.2.2 人工智慧故障診斷法--------------------------10
第三章 類小腦神經網路故障診斷法---------------------11
3.1 神經網路簡介--------------------------------11
3.1.1 生物神經元模型---------------------------11
3.1.2 人工神經元簡介及運算----------------------12
3.1.3 類神經網路基本架構------------------------14
3.2 類小腦神經網路理論----------------------------16
3.3 類小腦模式神經網路診斷系統之建立----------------17
3.3.1 輸入訊號之量化-------------------------------19
3.3.2 激發位址、編碼與輸出值------------------------20
3.3.3 激發記憶體權值調整---------------------------20
3.3.4 收斂性、學習效果評估-------------------------21
3.3.5 容錯能力-----------------------------------22
3.3.6 類小腦模式神經網路診斷流程-------------------22
第四章 案例研究------------------------------------30
4.1 水循環系統故障診斷案例------------------------30
4.1.1 水循環系統及運作原理----------------------30
4.1.2 水循環故障診斷系統----------------------32
4.1.3 水循環故障診斷系統結果及分析---------------34
4.2 大型汽輪機診斷案例----------------------------42
4.2.1 大型汽輪發電機簡介及說明----------------------42
4.2.2 大型汽輪發電機故障診斷系統--------------------43
4.2.3 大型汽輪發電機故障診斷系統結果及分析-----------45
第五章 軟、硬體技術之實現----------------------------56
5.1 軟體撰寫----------------------------------------56
5.2 硬體電路設計------------------------------------61
5.2.1 電源--------------------------------------62
5.2.2 晶片驅動及燒錄電路---------------------------63
5.2.3 顯示電路及矩陣式鍵盤-------------------------64
第六章 遠端故障診斷系統的實現------------------------72
6.1 遠端診斷系統概述---------------------------------72
6.2 系統之建立--------------------------------------74
第七章 結論及未來研究方向----------------------------77
參考文獻 ---------------------------------------------79
附錄 -------------------------------------------------82
自傳 -------------------------------------------------83

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