An Analysis Method For Gearbox Fault Diagnosis Based On Mathematical Morphology

Gearbox is the essential component of mechanical power transmission, it directly affect the operational status of the whole machinery. In order to ensure the normal operation of machinery, the gearbox condition monitoring, fault diagnosis and life prediction is of great significance.Gearbox fault diagnosis is essentially a process of signal processing, extract the fault feature information from the fault signal effectively is the key to fault diagnosis. Mathematical morphology is a nonlinear signal processing method which developed in recent years, and it has began to be applied in mechanical fault diagnosis fild gradually and has achieved good results, This paper sets gearbox as research object, and takes Gearbox fault type, pattern recognition and life prediction based on mathematical morphology into study. The main work and research results are as follows:(1)Common mechanical parts gear in gearbox and bearing vibration signal fault diagnosis principle are introduced, and the gearbox fault features are analyzed.(2)The influence of different structural elements and different scales for filtering is analyzed, the advantages of morphological filtering method and the traditional filtering method is Compared. For the Extreme value point selecting structural elements scale issue in the adaptive multiscale compound morphological filtering method, It proposed a limit threshold value adaptive multi-scale morphological filtering method, And then conduct filter operation on gear vibration signal using this method. Through the support vector machine(SVM), It realized the gear fault classification. Finally the effectiveness of the method is verified by simulation and experimental data.(3)The mathematical morphology single fractal fault diagnosis methods is Analysised, the gear single failure recognition ability of Single fractal dimension is Verified, and Mathematical morphology multi-fractal dimension is applied to quantitative analysis of gear compound faults, Experiments verified that the morphological multifractal dimension can well diagnose composite fault of gear box, Due to multi-fractal dimension exists spatial fluctuations and it can not recognize the different times of the same signal, proposed a high-dimensional fractal fault diagnosis method, This method is based on multi-fractal. It is a extension from the“line”to“flat”of morphologic fractal dimension, Using differential-based empirical mode decomposition to rise the signal dimension. It achieved fault diagnosis of high-dimensional signal by fractal dimension, Meanwhile, verified the high-dimensional fractal dimension fault diagnosis method using the entire life cycle gearbox bearings signal this method improves the fault recognition ability of multifractal effectively, and can distinguish the fault state, judge fault type nicely.(4)It can well Learn its operating status and remaining life by gearbox condition monitoring and fault forecast, which can deal with the fault occueed in operation processing, and extend the service life. Extracting feature by generalized mathematical morphology fractal spectral parameters, this method takes mathematical morphology fractal as theoretical basis, and defines the fractal spectrum and fractal spectrum parameters, The fractal spectrum parameters can quantitatively reflect the rolling bearing performance degradation degree. The effectiveness of the method is verifed using simulation and experimental signal. In order to accurately fitting the overall trend of the Rolling bearing performance degradation processes and the random Variation rule, the gray Markov model is applied to the prediction of the remaining life of rolling bearings, thus establishing a remaining life predict method based on Mathematical morphology fractal spectrum and Grey Markov Model.