Study For The Method Of Gear Fault Diagnosis In Reducer

As the core of power transmission in gearbox, gears are extensively used in the mechanical equipment, its damage and failure often cause trouble of transmission system or complete equipment, thus lead the great accident. So as the primary object of condition monitoring and fault diagnosis of gearbox, gears are attached more and more importance to. Condition monitoring and fault diagnosis of gears are mostly based on the analysis and study on its vibration signal during the productive process. But much unknown noise doping in the acquired real data certainly will effect on the accuracy of the analysis on gear vibration signal seriously. To solve this problem, the characteristics extracting and style identifying on gear fault are deeply studied in this article. And the main contents are as follows:(1) Beginning with the mechanism of gear vibration, the causes and characteristics of gear vibration are studied in the article. So does different compositions of frequency and the features of modulation signal caused by gear vibration;(2) Theories on wavelet transform and the characteristics of de-noise methods by different wavelet thresholds are studied. Based on different regulations of threshold choosing, de-noise methods to simulation signal produced by computer are studied based on different regulations of threshold choosing, and concluding the most effective method which is in favor of the characteristics extracting of gear vibration;(3) Studied the definition, properties and fault diagnosis of gear of STFT and WVD which belong to time-frequency analysis methods. Compared the advantage and defect between STFT and WVD by analyzing simulation signal produced by computer. And studied the strength of SPWV which is the advance of WVD during the time-frequency characteristics extracting of signal;(4) Combined wavelet packet node threshold de-noise with SPWV effectively, then analyzed the noise decreasing and time-frequency characteristics extracting of practical signal which comes from the gear working in different status. Got an effective method of gear fault diagnosis compared with the characteristics of gear fault.