Wavelet Transform And Empirical Mode Decomposition Of Rotor Vibration Signal Processing Method Based On

Motor is the important power energy and drive device in the process of modern industry, the motor rotor failure will directly affect the function accuracy, reliability and durability of the motor. Rotor vibration signals contain the important information of rotor operation, thus analyzing rotor vibration signals to obtain motor running characteristics is one of hot topics at present.According to the non-stationary characteristics of motor rotor vibration signals, in allusion to the existing problem that the traditional Fourier Transform can’t completely meet the requirements of fault signal feature extraction, the thesis focuses on the processing and analysis method of the wavelet transform, empirical mode decomposition and ensemble empirical mode decomposition to the motor rotor vibration signals. Main contents are as follows:1) According to the characteristics of rotor non-stationary vibration signals, the thesis studies the basic theory of wavelet transform, gives steps of the wavelet transformation to non-stationary vibration signal de-noising, the simulation experiment shows that the wavelet transform can realize the vibration signal de-noising effectively. For detection of mutation in the vibration signals, we study the singularity detection principle of wavelet transform deeply, gives the concrete steps of vibration signal singularity points positioning method. In the detailed diagram after wavelet decomposition, the detection effects of the singular part to the vibration signals are not obvious, a method of the detail signal multiplication is put forward, we realize the accurate positioning of vibration signal singularity point effectively.2) According to problems of the wavelet transform in the resolution, such as mistiness and blurring, the basic theory of the empirical mode decomposition method is proposed, the basic steps of empirical mode decomposition algorithm are presented, we demonstrate the completeness and orthogonality of empirical mode decomposition method, realize the signal trend extraction. In view of adaptive filtering method for empirical mode decomposition, the experiment proves that the method can removes noise effectively under the condition of low signal-to-noise ratio and can extract the useful components of the vibration signals. In order to extract fractional frequency components accurately in rotor fault signals, accurately extract fraction times frequency component of rotor fault signal, the thesis puts forward a method that we decompose the signal empirical mode firstly, then find out Hilbert marginal spectrum to the intrinsic mode function and realize the accurate judgment of rotor fault types at last.3) In view of mode mixing phenomenon when the empirical mode decomposition method decomposes signals, we put forward the ensemble empirical mode decomposition, give the concrete steps of this method. This method is verified by simulation study, it can solve the modal aliasing phenomenon effectively. On the basis of this, the applicability of wavelet transform, empirical mode decomposition and ensemble empirical mode decomposition to the motor rotor vibration signal de-noising are analyzed in detail, it confirms that the ensemble empirical mode method can eliminate noise effectively, suppress impulse interference and realize the noise elimination of vibration signals.4) The acquisition system of motor rotor vibration signals is established, based on the software development platform of Lab VIEW, cooperating with some hardware equipment, such as rotor vibration simulation test bench, sensors, signal conditioner and data acquisition card, the thesis establishes virtual instrument system of the motor rotor vibration signal acquisition and processing and completes a series of functions such as signal acquisition, display and save to the wrong-oil film whirl coupling, rotor rub-impact oil film whirl coupling. Using wavelet transform, empirical mode decomposition and ensemble empirical mode decomposition to get rid of the rotor noise in vibration signals, to extract useful feature of the rotor vibration signal, to identify the rotor fault location and type, and to implement the position of the singularity point in the rotor vibration signal.