The Study Of Local Mean Decomposition And Its Application In Automatic Fault Diagnosis Of Rolling Bearings

Rolling bearing is a core component of supporting the rotor in rotating machinery and directly relates to the stability and reliability of mechanical equipment. So it is very important to pay attention to condition monitoring and fault diagnosis about rolling bearing. Local Mean Decomposition(LMD) which is proposed by Jonathan S.Smith in 2005 is a new time-frequency analysis method for extracting fault feature in rolling bearing fault diagnosis. This method could decompose the original signals adaptively into a set of product functions(PF), thus, it could obtain the complete time-frequency distribution of the original signals and have physical meaning frequency in any time. Local Mean Decomposition has a unique advantage in signal processing, because this method do not require the vibration and do not lose date after decomposed.First, the general situation and development trend of the mechanical equipment condition monitoring and fault diagnosis are discussed, the research status and development trend at home and abroad of the rolling bearing status monitoring and fault diagnosis technology are detailed discussed. The time domain analysis method, the frequency domain analysis method and the time-frequency analysis are compared. The research status and development trend of the Local Mean Decomposition are elaborated primarily. Forms and reasons of the failure of the rolling bearing are analysed. The estimation formulas of the rolling bearing fault feature are established.Second, principle and algorithm of the Local Mean Decomposition are focused study and several concepts with the relevant to the Local Mean Decomposition are introduced. Through the simulation, this paper verifies the feasibility of the Local Mean Decomposition. The deficiencies of the Local Mean Decomposition are improved. For the selective problem of moving average step size, this paper proposes the moving average step length based on the smoothness of choice act and use the energy ratio of the power spectrum of low frequency part and high frequency part to measure the smoothness of the smoothing function. This paper combines the mirror continuation method with the cubic spline interpolation to slove the problem of end effect and verifies the feasibility and effectiveness of the Local Mean Decomposition after improvement.This paper also studies the local mean decomposition in the decomposition problem of the actual vibration signal. This paper makes comparison, analysis, and statistics about the original and improved algorithm of the local mean decomposition in terms of actual vibration signal analysis and summarizes some rules of the decomposed signals. According to the characteristics of rolling bearing fault signal and the method of spectrum kurtosis, this paper proposed the method of automatic fault diagnosis about rolling bearing which based on the local mean decomposition and the kurtosis. By the decomposition and analysis of the vibration data of rolling bearing in industry field and laboratory, this method could reduce the impact of the phenomenon of large amplittude in part and extract the fault features much more accurate from product functions. This paper provides the effective method for the automatic fault diagnosis of rolling bearing.