Research On Fault Diagnosis Methods For Rotating Machinery Based On Empirical Mode Decomposition And Support Vector Machine

The process of machinery fault diagnosis includes the acquisition of information and extracting feature and recognizing conditions of which feature extraction and condition identification are the priority. A novel method of time-frequency analysis, Empirical Mode Decomposition (EMD) and the comparatively recent development of pattern recognition techniques, Support Vector Machines (SVMs), are combined and applied to the rotating machinery fault diagnosis. EMD is based on the local characteristic time scale of signal and decompose the complicated signal into a number of Intrinsic Mode Functions (IMFs). By analyzing each IMF component that involves the local characteristic of the signal, the characteristic information of the original signal could be extracted more accurately and effectively. In addition, the frequency components involved in each IMF not only relates to sampling frequency but also changes with the signal itself, therefore, EMD is a self-adaptive time frequency analysis method that is applicable to non-linear and non-stationary processes perfectly thus overcoming the limitations experienced by the Fourier Transform (FT). In addition the EMD has a high signal-noise ratio (SNR). According to the non-stationary vibration signal characteristics of rotating machinery EMD method is introduced into rotating machinery fault diagnosis. The EMD method is improved upon and five types of feature extraction methods based on IMFs are proposed. SVMs have better generalization than Artificial Neural Networks (ANNs) and guarantee the local optimal solution is exactly the global optimal solution. SVMs can solve the learning problem of a smaller number of samples. Due to the fact that it is difficult to obtain sufficient fault samples in practice, SVMs are introduced into rotating machinery fault diagnosis due to their high accuracy and good generalization for a smaller sample number. The experimental results demonstrate the proposed diagnosis approach in which EMD and SVM are combined is effective.The outline of the work is as follows:1. The applications and limitations of conventional time frequency analysis method in signal processing are briefly discussed. A new theory of time frequency analysis method, Hilbert-Huang Transform (HHT), which includes EMD and Hilbert transform, is introduced. The analysis results from simulation signals show the decomposition effect of EMD is superior to that of the wavelet method; Hilbert spectrum obtained by HHT has a higher resolution than wavelet spectrum; Hilbert marginal spectrum has ahigher resolution than that obtained by FT.2. To target the disadvantage that the end effects will occur when EMD method and Hilbert transform are used to analysis signals, Radial Basis Function (RBF) Networks are introduced to prolong the original signal. The analysis results from simulation signals show that the proposed method effectively restrains the end effects.3. The limitations of the conventional statistical pattern recognition methods and ANNs classifier are targeted. SVMs are introduced into rotating machinery fault diagnosis due to the fact that it is hard to obtain enough fault samples in practice. This dissertation offers a comparison between two classification algorithms, ANNs and SVMs for cases where only limited training samples are available for diagnosis. The results show that SVMs have better performance than ANNs both in training speed and recognition rate.4. The concept of IMFs energy entropy is proposed and an approach of fault feature extraction based on IMFs energy entropy is put forward. The energy of vibration signals in different frequency bands will change when faults occur, thus one or more kinds of energy variation in frequency components indicate fault occurrence. The analysis results from roller bearing vibration signals show that the fault diagnosis approach based on IMFs energy entropy and SVMs can extract fault features effectively and classify working condition and fault patterns accurately.5. The concepts of Hilbert marginal energy spectrum and local Hilbert marginal energy spectrum are proposed and an approach of fault feature extraction based on local Hilbert marginal energy spectrum is put forward. Hilbert spectrum offers a complete time-frequency distribution, Hilbert marginal energy spectrum offers an energy distribution in the frequency domain and local Hilbert marginal energy spectrum offers an energy distribution in intrinsic frequency band. The analysis results from roller bearing vibration signals show that the fault diagnosis method based on local Hilbert marginal energy spectrum can extract fault features effectively and classify working condition and fault patterns accurately.6. Hilbert marginal spectrum reflects the amplitude distribution in the frequency domain accurately. To highlight the changes of energy in the intrinsic frequency band, an approach of fault feature extraction based on Hilbert marginal spectrum is proposed. The analysis results from roller bearing vibration signals shows that the fault diagnosis approach based on Hilbert marginal spectrum can extract fault features effectively and classify working condition and fault patterns accurately.7. Targeting the characteristics that periodic impulses usually occur whilst the...