機(jī)械外文翻譯---機(jī)械狀態(tài)監(jiān)測(cè)和故障診斷的最新進(jìn)展(存儲(chǔ)版)
【正文】 n frequency and vibration generation, and drew up the table of the vibration fault reasons, mechanism and recognition features for subsynchronous, synchronous and supersynchronous vibrations. Based on the table they proposed, they have classified the typical failures into 10 types and 58 kinds, and provided preventive treatments during the machine design and manufacture, Installation and maintenance, operation, and machine degradation. Xu et al. [11] concluded the mon faults of the rotational machines. Chen et al. [12] used the nonlinear dynamics theory to analyze the key vibration problems of the generator shaft. They established a rotor nonlinear dynamic model for the generator to prehensively investigate the rotor dynamic behavior under various influences, and proposed an effective solution to prevent rotor failures. Yang et al. [13] adopted vibration analysis to study the fault mechanism of a series of diesel engines. Other 外文翻譯原文及譯文 6 researchers have done a lot in the fault mechanism of mechanics since 1980s, and have published many valuable papers to provide theory and technology supports in the application of fault diagnosis systems [1418]. However, most of the fault mechanism research is on the qualitative and numerical simulation stage, the engineering practice is difficult to implement. In addition, the fault information often presents strong nonlinear, non stationary and non Gaussian characteristics, the simulation tests can not reflect these characteristics very accurately. The fault diagnosis results and the application possibility may be influenced significantly. As a result, the development of the fault diagnosis technique still faces great difficulties. 山東交通學(xué)院畢業(yè)設(shè)計(jì) 7 3. Advanced Signal Processing and Feature Extraction Methods Advanced signal processing technology is used to extract the features which are sensitive to specific fault by using various signal analysis techniques to process the measured signals. Condition information of the plants is contained in a wide range of signals, such as vibration, noise, temperature, pressure, strain, current, voltage, etc. The feature information of a certain fault can be acquired through signal analysis method, and then fault diagnosis can be done correspondingly. To meet the specific needs of fault diagnosis, fault feature extraction and analysis technology is undergoing the process from time domain analysis to Fourier analysisbased frequencydomain analysis, from linear stationary signal analysis to nonlinear and nonstationary analysis, from frequencydomain analysis to timefrequency analysis. Early research on vibration signal analysis is mainly focused on classical signal analysis which made a lot of research and application progress. Rotating mechanical vibration is usually of strong harmonic, its fault is also usually registered as changes in some harmonic ponents. Classical spectrum analysis based on Fourier transform (such as average timedomain techniques, spectrum analysis, cepstrum analysis and demodulation techniques) can extract the fault characteristic information effectively, thus it is widely used in motive power machine, especially in rotating machinery vibration monitoring and fault diagnosis. In a manner of speaking, classical signal analysis is still the main method for mechanical vibration signal analysis and fault feature extraction. However, classical spectrum analysis also has obvious disadvantages. Fourier transform reflects the overall statistical properties of a signal, and is suitable for stationary signal analysis. In reality, the signals measured from mechanical equipment are everchanging, nonstationary, nonGaussian distribution and nonlinear random. Especially when the equipment breaks down, this situation appears to be more prominent. For nonstationary signal, some timefrequency details 外文翻譯原文及譯文 8 can not be reflected in the spectrum and its frequency resolution is limited using Fourier transform. New methods need to be proposed for those nonlinearity and nonstationary signals. The strong demand from the engineering practice also contributes to the rapid development of signal analysis. New analytical methods for nonstationary signal and nonlinear signal are emerging constantly, which are soon applied in the field of machinery fault diagnosis. New methods of signal analysis are main including timefrequency analysis, wavelet analysis, HilbertHuang
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