研究生: |
陳志忠 Jhih-Jhong Chen |
---|---|
論文名稱: |
感應交流馬達旋轉設備振動特徵分析 Vibration Signature Analysis of Induction Motor Driven Machinery |
指導教授: |
劉孟昆
Meng-Kun Liu |
口試委員: |
藍振洋
Jhen-Yang Lan 項天瑞 Tien-Ruey Hsiang 劉孟昆 Meng-Kun Liu |
學位類別: |
碩士 Master |
系所名稱: |
工程學院 - 機械工程系 Department of Mechanical Engineering |
論文出版年: | 2017 |
畢業學年度: | 105 |
語文別: | 中文 |
論文頁數: | 115 |
中文關鍵詞: | 旋轉機械 、失效模型 、軸承 、振幅調製 、快速傅立葉轉換 、共振頻率 、包絡譜 、放電破壞 |
外文關鍵詞: | rotating machine, failure modes, bearing, amplitude modulation, Fast Fourier Transform(FFT), resonance frequency, envelope spectrum, degradation of electric discharging |
相關次數: | 點閱:773 下載:0 |
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迄今,諸多旋轉機械設備皆需要靠馬達來進行動力傳輸,又以感應交流馬達為大宗。隨著長時間的持續運作,其內部軸承會漸漸產生初期損壞,接著便開始擴大損壞範圍,情況嚴重將連帶造成整組機械損毀,因此前人便研究整個機械內部可能發生的損壞方式,並歸納出許多失效模型,供後人研究如何有效地偵測,甚至能夠預測這些損壞行為,達成設備診斷與零組件提前汰換的動作。
為了從訊號上瞭解這些失效模型的表現,許多研究人員透過感測器來量測旋轉機械運作時,故障軸承之振動訊號或馬達故障之電流電壓訊號等其他方式,與正常狀態的訊號做比較,期望能應用在實際工廠需運作不間斷的旋轉設備上。
本研究先整理了旋轉機械振動相關之失效模型,討論其物理意義與訊號之頻率域上的表現。許多研究探討著利用不同訊號分析方式區別正常與損壞軸承之振動訊號,以提供旋轉機械初期損壞的判斷準則。旋轉設備中,常因軸電壓而形成軸承電流,造成電蝕現象,間接使電動機械損壞,藉此有研究於泵浦全新軸承上通以電流,並量測其運轉過程中的振動幅值變化,以此法制定軸承初期損壞的振動門檻。因此,我們也製造損壞軸承和其他旋轉機械失效模式,觀察其時間域、頻率域、包絡譜的訊號表現。由於軸承內外環與其缺陷頻率的振幅調製(Ampiltude Modulation,AM)現象,故透過軸承的內外環敲擊測試與掃頻實驗找尋其自然共振頻率,做為包絡譜分析中帶通濾波範圍的依據,接著執行軸承放電破壞實驗,並分析其每筆資料能否找到對應的失效模式訊號,以制定一個停機門檻。
Many rotating machines are driven by induction motors and the life of induction motors usually lasts for decades. However, during certain conditions such as load variation, the bearing would suffer fatigue failures. These failures begin from incipient faults and would cause catastrophic disasters if unattended. Therefore, previous literatures concluded several failure modes of the rotating machine and developed condition monitoring techniques to achieve fault diagnosis and replacing abnormal components in advance.
In order to understand the behavior of failure modes, several sensors were installed on the rotating machine in previous literatures to measure the vibration, current, and voltage of the bearing fault condition, and compared the measurement to the normal condition. The knowledge could be applied to the monitoring of the non-stop rotating machine in the industry.
The failure mode and effect analysis of the rotating machine failures were elaborated in this thesis. Both the physical definition and frequency spectrum of the failure were discussed. The criterion to determine the machine condition were proposed by applying signal analysis technique on the vibration signal of the normal and faulty bearing. The electrical discharge through the oil film between the shaft and bearing were generated by applying AC current to the shaft and housing, and the time-domain, frequency spectrum, and enveloping spectrum of the vibration signals were analyzed. Amplitude modulation could be observed when there are inner and outer ring faults. Through the impact test and the sweeping test, the resonant frequency of the bearing could be found and used to develop enveloping spectrum. The fatigue failure test was conducted and an amplitude threshold was set.
[1]Siyambalapitiya, DJ Tilak, and Peter G. Mclaren. "Reliability improvement and economic benefits of online monitoring systems for large induction machines." IEEE transactions on industry applications 26.6 (1990): 1018-1025.
[2]BENBOUZID, M. El Hachemi. A review of induction motors signature analysis as a medium for faults detection. IEEE transactions on industrial electronics, 2000, 47.5: 984-993.
[3]Tandon, N., G. S. Yadava, and K. M. Ramakrishna. "A comparison of some condition monitoring techniques for the detection of defect in induction motor ball bearings." Mechanical systems and signal processing 21.1 (2007): 244-256.
[4]MOBLEY, R. Keith. Vibration fundamentals. Butterworth-Heinemann, 1999.
[5]LACEY, S. J. An overview of bearing vibration analysis. Maintenance & Asset Management, 2008, 23.6: 32-42.
[6]朱效賢. 包絡譜分析於軸承故障診斷之探討暨工程應用. 中央大學機械工程學系學位論文, 2005, 1-97
[7]GRANEY, Brian P.; STARRY, Ken. Rolling element bearing analysis. Materials Evaluation, 2012, 70.1: 78.
[8]RAI, Vivek Kumar; MOHANTY, A. R. Condition monitoring techniques for rolling element bearings: an overview. In: Proceedings of National conference on role of NDE in modern maintenance management, IACR, Rayagada, Orissa (India). 2005. p. 8-18.
[9]RAI, V. K.; MOHANTY, A. R. Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert–Huang transform. Mechanical Systems and Signal Processing, 2007, 21.6: 2607-2615.
[10]LI, Bo, et al. Neural-network-based motor rolling bearing fault diagnosis. IEEE transactions on industrial electronics, 2000, 47.5: 1060-1069.
[11]TANDON, N.; CHOUDHURY, A. A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings. Tribology international, 1999, 32.8: 469-480.
[12]LESSMEIER, Christian, et al. Data Acquisition and Signal Analysis from Measured Motor Currents for Defect Detection in Electromechanical Drive Systems, 2014.
[13]HARIHARA, Parasuram P.; PARLOS, Alexander G. Using motors to detect degradation. World Pumps, 2011, 2011.11: 24-29.
[14]BOYD, J.; KAUFMAN, H. N. The causes and the control of electrical currents in bearings. Lubrication Engineering, 1959, 15.1: 28-35.
[15]Jaafar Alsalaet, Vibration Analysis and Diagnostic Guide, 2012.
[16]TPI技術手冊,軸承的檢測與診斷.
[17]STI Field Application Note, Rolling Element Bearings,2012.
[18]Jaafar Alsalaet, Vibration Analysis and Diagnostic Guide, 2012.
[19]YAN, Ruqiang; GAO, Robert X. Hilbert–Huang transform-based vibration signal analysis for machine health monitoring. IEEE Transactions on instrumentation and measurement, 2006, 55.6: 2320-2329.