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Research on Fault Diagnosis Method of Rolling Bearing Based on Hilbert-Huang Transform

Author: ZhouChuan
Tutor: WuXing
School: Kunming University of Science and Technology
Course: Mechanical and Electronic Engineering
Keywords: Rolling bearings Hilbert - Huang Transform Empirical Mode Decomposition Morphological filtering Support Vector Machine
CLC: TH165.3
Type: Master's thesis
Year: 2010
Downloads: 256
Quote: 0
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Abstract


Rolling bearings are widely used in rotating machinery, the vulnerable parts of rotating machinery. Many faults of rotating machinery and rolling bearings. Roller bearing fault will cause the machine to produce abnormal vibration and noise, and even cause damage to the machine and cause a major accident casualties. Research rolling bearing fault diagnosis method has great significance. Rolling bearing fault vibration signal with non-stationary characteristics of engineering in the number of fault sample usually less traditional fault feature extraction methods and the effect is not ideal ball bearing fault diagnosis based on the failure of the theory of neural network pattern recognition method. Therefore, the use of non-stationary signal analysis method, Hilbert-Huang transform (HHT), and combined morphological filtering, two-dimensional spectral entropy, and support vector machine (SVM) bearing fault feature extraction and diagnostic methods. HHT as a theoretical basis around the HHT theory, fault feature extraction methods, pattern recognition method and fault feature extraction and diagnosis system for four themes, theoretical research, simulation and experimental validation of the combined line of research, as well as a prototype system, The main contents are as follows: (1) of the theory of HHT-depth study of the typical frequency analysis methods, such as short-time Fourier transform, Wigner-Ville distribution and wavelet transform comparative analysis to verify the HHT analysis of non-stationary the effectiveness and superiority of the signal. Empirical mode decomposition (EMD), the end effect phenomenon, the the estimated extension improved algorithm based on the endpoint extreme points; against noise caused by the EMD modal cleavage and false modal problems, based on generalized morphological filtering and correlation coefficient an improved method. Both improved the effectiveness of the method is verified by simulation and experiment, and thus improve the HHT Roller Bearing Fault Diagnosis. (2) for failure bearing vibration signal with non-stationary, high-frequency modulation and vulnerable to background noise and low-frequency harmonic interference proposed fault feature extraction method based on EMD and adaptive morphological filtering and demodulation. This method uses EMD separation of high-frequency modulation signal, then demodulated using adaptive morphological filter based on kurtosis fault feature extraction of non-stationary signals. Simulation study and bearing outer ring fault experimental results show that this method can effectively extract the bearing fault features, the the Hilbert envelope analysis and better than, and has good application prospects in the rolling bearing fault feature extraction. (3) on the basis of the signal 2D spectral entropy, genetic algorithms and SVM research, respectively, based on two-dimensional spectral entropy EMD fault feature extraction methods and genetic algorithm based SVM fault classification method. Rolling bearing fault diagnosis experiment results show that the combination of these two methods for fault diagnosis, high diagnostic accuracy can be obtained. This method provides a new idea to the rolling bearing fault diagnosis. (4) based on the study of the rolling bearing fault feature extraction and diagnostic methods, the development of a rolling bearing fault feature extraction and diagnosis prototype system using Matlab. Simulation and experimental signals verify the effectiveness and practicality of the system, it provides an example of the development of the rolling bearing fault diagnosis system.

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CLC: > Industrial Technology > Machinery and Instrument Industry > Machinery Manufacturing Technology > Flexible manufacturing systems and flexible manufacturing cell > Fault diagnosis and maintenance
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