Diagnosis method for impact type failure between rolling bearing and gear based on optimal self-adaptive wavelet filter

Abstract

The invention relates to a diagnosis method for impact type failure between a rolling bearing and a gear based on an optimal self-adaptive wavelet filter, which comprises the following steps: firstly establishing an exponentially damped sinusoidal impact type failure signal model, using a mode self-adaptive wavelet generation algorithm publicly disclosed by Hector Mesa to generate a self-adaptive wavelet filter which is matched with the signal model, then utilizing a fast FIR (finite impulse response) filtering algorithm to execute the wavelet filter to obtain the filtering result, then calculating the kurtosis value of the filtering result, and using an evolution differential algorithm to repeat the steps so as to finally obtain an optimized envelope spectrum. By adopting the method, impact failure characteristics in vibration signals can be precisely extracted, and a clearer envelope spectrum can be given out, thereby ensuring that failure symptoms can be displayed more clearly.

Description

technical field [0001] The invention belongs to the field of equipment fault diagnosis, and in particular relates to a rolling bearing and gear impact fault diagnosis method based on an optimal self-adaptive wavelet filter. Background technique [0002] In the field of equipment fault diagnosis, because rolling bearings and gears are very commonly used and very important parts, and both of them are one of the most easily damaged parts in mechanical equipment, it is of great importance to carry out condition monitoring and fault diagnosis on them. Engineering significance. When there are defects in rolling bearings and gears, periodic shocks will occur when the damaged point comes into contact with other components. How to effectively extract impact features from vibration acceleration signals is a key issue for judging the position and loss degree of rolling bearing and gear defects. [0003] Traditional fault diagnosis methods for rolling bearings and gears include spectr...

AI Technical Summary

Problems solved by technology

However, frequency spectrum analysis needs to use more orthogonal simple harmonic trigonometric functions to approximate the impact signal. Unless the bearing is seriously damaged and the fault features are very obvious, otherwise, for the vibration signals of rolling bearings and gears with complex components, the fault impact features are often included in the frequency spectrum. Overwhelmed by background noise and other signal components

Envelope demodulation is an effective method for fault diagnosis of rolling bearings and gears, but the envelope demodulation must first perform band-pass filtering or high-pass filtering on the vibration signal; how to reasonably select relevant parameters for different units and different working conditions is Difficulties in engineering, on-site engineers are often difficult to grasp the essentials of setting, and there are problems in the versatility of each specific setting

In addition, time-frequency analysis, such as Wegener Division, EMD-HHT, etc., has also been reported to be used for the diagnosis of rolling bearing and gear impact faults, but these methods have problems such as large amount of calculation, difficult parameter selection, and difficulty in real-time monitoring.

At the same time, these methods also require engineers to have relatively deep signal processing knowledge, which also makes their application difficult in engineering practice.

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