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Gearbox is an important part of mining machinery, and its working performance often affects whether the entire mining machinery and equipment can work normally. Therefore, its performance is particularly important for large mining machinery. Common faults in gearboxes occur mostly in gears and bearings. The most common method for fault diagnosis of gearboxes using vibration signals is also convenient for data processing and analysis. The internal structure of the gearbox is complex, the vibration source is complex and variable, and it is difficult to determine the frequency component of the vibration signal of the gearbox.
The vibration signal of the gearbox during the acceleration phase tends to contain more fault information than the constant speed, so in the field of construction machinery, the acceleration and deceleration test is usually used as part of the standard mechanical testing process. However, the vibration signal of the acceleration process belongs to the non-stationary signal, and the change of the rotational speed is manifested in the fact that the frequency component on the spectrogram is ambiguous. This blurred frequency component will cause errors in the amplitude measurement, making it difficult to achieve effective effect in traditional spectrum analysis. . If such signals are artificially assumed to be stationary signals for processing, the result will be severe frequency ambiguity.
In the rotating machine, as the speed of the shaft changes in the speed phase, its vibration signal is closely related to the speed. The step ratio analysis extracts the harmonic components closely related to it according to the speed signal, filters the unrelated noise and raises the fund item. The traditional order spectrum analysis has low recognition information for faulty gears, and the autoregressive parameters of the AR model are very sensitive to the change of fault signals. It contains a large number of fault characteristics information, which can be used for fault diagnosis of gearboxes. Through the analysis and comparison of the measured vibration signals of the gearbox, the method has fast recognition speed and high fault recognition rate. It is verified that the combination of the order ratio AR model and the SVM can be effectively applied to the gearbox gear fault diagnosis, and the AR model is extended. The scope of use provides a new method for fault diagnosis of gearboxes.