Rotary kiln holder wheel bearing fault diagnosis method based on wavelet packet decomposition

Abstract

The invention discloses a rotary kiln holder wheel bearing fault diagnosis method based on wavelet packet decomposition. The method comprises the following steps: establishing a bearing diagnosis model, and according to an actual running condition, classifying bearing faults; obtaining vibration signals of bearings; and performing denoising processing on the vibration signals; selecting a packet primary function, performing four-layer wavelet packet decomposition and reconstruction on each obtained vibration signal, and extracting signals of each frequency scope; solving fourth-layer wavelet packet frequency band energy of the vibration signals after the decomposition and calculating its maximum value, and taking a frequency band as a feature component; performing Hilbert demodulation analysis on the feature fault, and obtaining an envelope spectrogram of modulation signals; and making a comparison with an envelope spectrogram corresponding to a bearing normal running state so as to determine bearing fault types and fault positions. The rotary kiln holder wheel bearing fault diagnosis method based on the wavelet packet decomposition, provided by the invention, overcomes the disadvantages of low manual detection efficiency and inaccurate detection positions, can rapidly and accurately diagnosis the fault positions, reduces equipment fault time and improves the equipment integrated utilization rate.

Description

technical field [0001] The invention relates to the field of construction machinery fault diagnosis, in particular to a fault diagnosis method for a supporting wheel bearing in a rotary pit based on wavelet packet decomposition. Background technique [0002] The rotary kiln belongs to building materials equipment, and it is divided into cement rotary kiln, metallurgical chemical rotary kiln and lime rotary kiln according to the different processing materials. The rotary kiln is mainly composed of transmission device, cylinder body, support device and movable kiln head and other components. [0003] Since the rotary kiln has a sealed structure and the overall structure is complicated, it is impossible to use accurate diagnostic methods such as establishing mathematical models to find fault points, which brings certain difficulties to fault diagnosis. [0004] There are many types of faults in the rotary kiln, and the characteristics of many fault types are relatively vague, ...

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to address the above technical problems and provide a fault diagnosis method based on wavelet packet decomposition for supporting wheel bearings in rotary pits. Fault diagnosis efficiency, reduce equipment failure time, improve comprehensive utilization of equipment

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