지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학위논문
- 저자정보
- 지도교수
- 이장현
- 발행연도
- 2014
- 저작권
- 인하대학교 논문은 저작권에 의해 보호받습니다.
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초록· 키워드
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The fault diagnosis in machinery is started by the signal processing of the sensored factual data such as vibration signals which are symptomatic for the operation condition of the machine. Thereafter, the processed data is interpreted to determine the machinery fault existence and identify specific component inside machine that are degrading. As machinery’s abnormal vibration signal contains its fault information, proper analysis of its vibration signal can diagnose the machinery fault. Vibration signals acquired from an industry vibration transducer, such as an accelerometer are processed by Morlet wavelet. Time-frequency spectrum result obtained by Morlet wavelet transformation is the analysis of the real signal in both frequency and time domain. Special attention is given to the common vibration causes like unbalance and bearing defects in the rotating machines installed in offshore plants. Analysis performed using simulated signal proves that time-frequency contour map has the capabilities for fault identification. Thereafter the root-cause analysis of the original vibration problem, so called failure diagnosis, can be performed using the time-frequency contour map. Acceptance limits for normal operation is decided by the application of vibration standards and vibration alarm limits given by vibration severity guidelines. A Graphical User Interface (GUI) is executed to control the diagnosis assessment process. Also, a case study about the implementation of the continuous wavelet transform to the centrifugal compressor fault diagnosis is discussed.
목차
1. Introduction 11.1 Background of the Study 11.2 Motivation and Scope of Research 21.3 Organization of Thesis 31.4 History of Art 52. Methodology 72.1 Offshore Equipment 82.1.1 Introduction 82.1.2 Comp 92.1.3 Uses of Air Compressor on Offshore Plant 102.2 Common Causes of Machinery Breakdown 132.2.1 Unbalance 142.2.2 Misalignment 152.2.3 Bearing Failure 162.3 Evolution of Maintenance Strategies 172.4 Evolution of Wavelet Transform 202.4.1 Fourier Transform (FT) 202.4.2 Short Time Fourier 212.4.3 Wavelet Transform 232.4.4 Comparative Visualization 243. Wavelet Transform 273.1 Wavelet Definition 273.2 Wavelet Characteristics 273.3 Wavelet Transform as a Signal Processing Tool 283.4 Wavelet Selection Criteria 313.5 Morlet Wavelet Transform Theory 324. Vibration Monitoring 354.1 Condition Monitoring Techniques 354.2 Vibration Monitoring 355. Case Study 396. Conclusion 47References 48
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