臺灣博碩士論文加值系統

本論文提出一個針對多重輸出入的轉子軸承系統且受雜訊污染狀況下的混合式系統識別方法來有效提升識別精度。
混合法整合了卡曼濾波器(Kalman filter)及特徵系統實現法則(ERA)以決定系統的狀態空間模型。為了更有效地提高高頻模態的系統識別精度,亦提出第二種利用參雜最佳化訊號的系統識別方法來強化特徵系統實現法則(ERA)並完成整個方法的完整性。參雜訊號是藉由整合最佳化方法與奇異值分解(SVD)兩法而來,透過理論的推導,吾人可以解釋SVD有效率除雜訊的條件及參雜訊號有效的理由。
為了驗證所提出的方法,文中採用了轉子軸承系統來解釋與展現其高雜訊免疫力以及利用穩態響應得到多重輸出入狀態空間模型的能力且不論系統是耦合或是含有高頻模態。模擬結果顯示所提出之參雜最佳化訊號的特徵系統實現法在與單獨SVD技術或整合傳統Butterworth 濾波器的SVD技術做比較,均可有效的改善系統識別的精度, 因此最後, 即使在很低的訊雜比下雜訊的效應也可以被減弱。

This thesis proposes a specific methodology for hybrid system identification. This system can be used with multi-input, multi-output (MIMO), noise-polluted rotor-bearing systems, so as to increase identification accuracy. This hybrid method, which integrates a Kalman filter with an eigensystem realization algorithm (ERA), was developed to determine state space models. To further increase the accuracy of identifying high-frequency modes of systems, another identification methodology was developed to enhance the eigensystem realization algorithm (ERA), which entails doping an optimum signal, thereby providing all the methodology necessary for dealing with such systems.

The doping signal is obtained by integrating an optimization technique with the singular value decomposition (SVD) technique. Through theoretical derivation, we can interpret the effectiveness of the optimum signal and the sufficient conditions for removing the noise by SVD.
To demonstrate the capability of proposed methods, a rotor-bearing system has been adopted to explain and show the high noise-immunity, the capability of using a steady state response and obtaining a MIMO state space model, no matter the system is coupled or with high frequency mode. Simulation results show that the proposed ERA with a novel optimum signal can significantly improve system identification accuracy in comparison with the SVD technique alone or with the SVD technique combined with a traditional Butterworth filter in system identification of high frequency modes. Finally, the noise effect can be attenuated even under a low signal/noise ratio (S/N).

中文摘要
ABSTRACT
NOMENCLATURE
CHAPTER 1 INTRODUCTION
1.1 Background and Motivation
1.2 Literature review
1.3 Objective of present reaserch
1.4 Scope of present research
CHAPTER 2 MATHEMATICAL MODEL OF HYDRODYNAMIC BEARING
2.1 Pressure distribution of hydrodynamic
bearings
2.2 Numerical simulation for the Reynold
equation
2.3 Equation of motion of a dynamic rotor
system
2.4 Linearization of hydrodynamic
bearing systems
CHAPTER 3 KALMAN FILTER AND ERA METHOD
3.1 Direct Eigensystem Realization Algorithm
(ERA) identification
3.2 Direct Kalman filter identification
3.3 Kalman filter & eigensystem realization
algorithm(KFERA) identification
3.4 Numerical simulation and discussion
3.5 Preliminary Conclusions
CHAPTER 4 OPTIMUM DOPING SIGANL METHOD
4.1 Singular Value Decomposition(SVD)
Filtering Techniques
4.2 Sufficient conditions for separating signal and
noise by SVD
4.3 The theoretical derivation of the sufficient
conditions for separating signal from noise by
SVD
4.4 System identification for high frequency mode
by doping an optimum signal
4.5 Numerical simulation and discussion
4.6 Preliminary Conclusions
CHAPTER 5 CONCLUSION
5.1 Summary conclusions
5.2 Future Study

APPENDIX A Derivation for Reynolds
Equation
APPENDIX B Exact Solution for Reynolds
Equation of Long Bearing
APPENDIX C Eigensystem Realization
Algorithm(ERA)
APPENDIX D Kalman Filter Method
APPENDIX E Othogonality of Phase Shift
Signal
LIST OF FIGURE
LIST OF TABLE
REFERENCE
作者學經歷及著作文獻

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