Maximum correlation kurtosis deconvolution method without period

Abstract

The invention discloses a maximum correlation kurtosis deconvolution method without a period. The method comprises steps of firstly, carrying out truncation and mean removal processing of an acquiredvibration signal; performing Hilbert transform of the vibration signal to obtain an analytic signal of the vibration signal; selecting a maximum value point of the part behind a first zero crossing point in the autocorrelation spectrum as an iteration period to carry out deconvolution operation; then, continuously calculating the maximum value point of the part behind the first zero crossing pointin the autocorrelation spectrum of the signal after each iterative filtering as an iterative period to update a filter so as to obtain an optimal filter coefficient; and lastly, performing envelope analysis of the filtered signal, and extracting the fault characteristic frequency from the envelope spectrum. The method is advantaged in that human participation is not needed in the extraction process of the characteristic frequency, automation of fault characteristic extraction and diagnosis monitoring can be achieved, the time is saved, and efficiency is higher.

Description

technical field [0001] The invention relates to the technical field of mechanical equipment fault diagnosis, in particular to a period-free maximum correlation kurtosis deconvolution method. Background technique [0002] Vibration analysis is one of the most effective methods for fault diagnosis of mechanical equipment at this stage, and the state degradation of mechanical equipment often manifests as changes or abnormalities in vibration information. At present, signal processing methods based on vibration information, such as time domain method, frequency domain method and time frequency domain method, have been successfully applied to bearing fault diagnosis and have produced very good results. However, there are still many challenges in the field of rolling bearing fault diagnosis, and the extraction of bearing faults still has many difficulties. 1. The lengthy and complicated transmission path between the test sensor and the fault source can seriously affect the transf...

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Problems solved by technology

However, there are still many challenges in the field of rolling bearing fault diagnosis, and the extraction of bearing faults still has many difficulties

1. The lengthy and complicated transmission path between the test sensor and the fault source can seriously affect the transfer function, thereby reducing the amplitude of the impact signal and prolonging the time, so that the pulse caused by the fault is easily covered by noise

2. The random fluctuation of the rollers in the bearing will further blur the spectral envelope spectrum of the original quasi-periodic fault shock

3. The influence of aperiodic noise and periodic interference from the mechanical system adds more challenges to extracting the impact of bearing faults

In the field of mechanical fault diagnosis, the two most commonly used deconvolution methods are the minimum entropy deconvolution method and the maximum correlation kurtosis deconvolution method, but the minimum entropy deconvolution method is easily affected by the randomness in the signal. Impact of shock interference, and the maximum correlation kurtosis deconvolution method can overcome the defect of random shock interference, but this method also requires accurate period as prior knowledge

In the fault diagnosis of rolling bearings, it is very difficult to accurately determine the faulty bearings in advance.

First of all, the rotation of mechanical equipment is difficult to keep completely constant, so the inaccurate calculation of the fault cycle caused by speed fluctuation is unavoidable; secondly, there are many parts in the equipment, and the number of rolling bearings is usually large, so it is difficult to determine the source of the fault in advance, so , it is not realistic to pre-calculate the period of the faulty bearing

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