Abstract
The fault feature extraction of gearbox is difficult to achieve under complex working conditions, and this paper presents a hybrid fault diagnosis method for gearbox based on the combining product function (CPF) and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) methods. First, ensemble local mean decomposition (ELMD) is utilized to reduce the noise in original signal, and get a series of product functions (PFs), through the correlation coefficient method to remove false components and residual components. Then, multi-point kurtosis of the definition is achieved by calculating the multi-point kurtosis spectrum of each layer PF, and the fault feature period is extracted and the PFs without periodic impact are removed. After that, in order to maintain the integrity of the original signal, the PFs with the same period are recombined by the combined product function method. Finally, the different cycle interval is configured, reduce the noise through MOMEDA on the combined signal, to further extract the fault feature. The method is applied to the feature extraction of gear box composite fault to verify the feasibility of this method.
Highlights
The gearbox is the most important power transmission component in mechanical equipment, and its health status directly affects whether mechanical equipment can work properly
This paper explores a method of extracting fault features of gearboxes based on combining product function (CPF)-multipoint optimal minimum entropy deconvolution adjusted (MOMEDA), through the double analysis of the simulation signal and the measured signal, and it identifies its fault characteristics accurately, which provides a new idea for extracting mechanical fault features
This is a noise component and removed, in order to enhance the energy of the fault feature, CPF1 was obtained by combining the first three layers of product functions (PFs), the combination of PF4 and PF5 yields the CPF2 results, and they are as shown in Figure 22, CPF1 fault cycle energy is strong here do not need analysis, CPF2 to further solve the multi-point kurtosis, the results are shown in Figure 23, obviously the bearing fault feature is significantly enhanced
Summary
The gearbox is the most important power transmission component in mechanical equipment, and its health status directly affects whether mechanical equipment can work properly. EEMD has been successfully applied in the fault diagnosis, but due to improper selection of the added white noise level, there is still the phenomenon of mode mixing, resulting in energy leakage. Used LMD and other fault diagnosis methods combined, successfully extracting the fault information, but the practice proved that when the LMD decomposition of the complex vibration signal cannot achieve satisfactory results, the main reason is that it has a modal aliasing phenomenon, the so-called modal aliasing refers to the same PF contains several time feature scales, or the same time scale is broken down in different PF, which leads to PF lost the original meaning of the original signal, and further leads to energy leakage or misdiagnosis occurred, the cause of this phenomenon is largely influenced by background noise .Based on these analysis, the noise component with the noise component and the PF with strong correlation with the original signal (including the PF produced by the modal aliasing) in the PF component is obtained by the LMD decomposition, and the noise component can be removed by the correlation coefficient.
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